{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 第一周作业\n",
    "# 导入模块\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets\n",
    "from tensorflow.keras import Input,Model\n",
    "from tensorflow.keras.layers import Flatten,Dense,Activation\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 5us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 9s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 4s 1us/step\n",
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "# 载入数据\n",
    "(x_train,y_train),(x_test,y_test) = datasets.fashion_mnist.load_data()\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(-1,28*28)\n",
    "x_test = x_test.reshape(-1,28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 归一化处理\n",
    "x_train = x_train/255.0\n",
    "x_test = x_test/255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务一¶\n",
    "### 搭建模型，要求设置两层隐层：\n",
    "\n",
    "### 第一层隐层设置：神经元个数256，初始化方法为glorot_normal，激活函数为tanh\n",
    "### 第二层隐层设置：神经元个数128，初始化方法为glorot_normal，激活函数为tanh\n",
    "### 然后，运行模型训练和测试集评估代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型搭建\n",
    "inputs = Input(shape=(28*28),name='input')\n",
    "# 隐层\n",
    "x = Dense(units=256,kernel_initializer='glorot_normal',name='dese_0')(inputs)\n",
    "x = Activation(activation='tanh',name='activate_0')(x)\n",
    "x= Dense(units=128,kernel_initializer='glorot_normal',name='dese_1')(x)\n",
    "x = Activation(activation='tanh',name='activate_1')(x)\n",
    "\n",
    "# 输出层\n",
    "outputs = Dense(units=10,activation='softmax',name='logit')(x)\n",
    "\n",
    "model = Model(inputs=inputs,outputs=outputs)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 784)]             0         \n",
      "_________________________________________________________________\n",
      "dese_0 (Dense)               (None, 256)               200960    \n",
      "_________________________________________________________________\n",
      "activate_0 (Activation)      (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dese_1 (Dense)               (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "activate_1 (Activation)      (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "logit (Dense)                (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 235,146\n",
      "Trainable params: 235,146\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务二\n",
    "#### 请尝试使用课上讲的提前终止的方法解决过拟合，使用到的api如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置EarlyStopping    防止过拟合\n",
    "earlystop = EarlyStopping(monitor='val_loss',min_delta=1e-4,patience=10,restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5062 - accuracy: 0.8168 - val_loss: 0.4917 - val_accuracy: 0.8253\n",
      "Epoch 2/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5040 - accuracy: 0.8185 - val_loss: 0.5040 - val_accuracy: 0.8194\n",
      "Epoch 3/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5063 - accuracy: 0.8176 - val_loss: 0.5198 - val_accuracy: 0.8124\n",
      "Epoch 4/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5052 - accuracy: 0.8171 - val_loss: 0.5546 - val_accuracy: 0.7965\n",
      "Epoch 5/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5052 - accuracy: 0.8181 - val_loss: 0.5128 - val_accuracy: 0.8186\n",
      "Epoch 6/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5053 - accuracy: 0.8182 - val_loss: 0.5069 - val_accuracy: 0.8136\n",
      "Epoch 7/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5032 - accuracy: 0.8188 - val_loss: 0.5206 - val_accuracy: 0.8096\n",
      "Epoch 8/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5020 - accuracy: 0.8203 - val_loss: 0.5225 - val_accuracy: 0.8086\n",
      "Epoch 9/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5033 - accuracy: 0.8198 - val_loss: 0.5477 - val_accuracy: 0.8019\n",
      "Epoch 10/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5041 - accuracy: 0.8178 - val_loss: 0.5050 - val_accuracy: 0.8228\n",
      "Epoch 11/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5017 - accuracy: 0.8192 - val_loss: 0.4993 - val_accuracy: 0.8200\n",
      "Epoch 12/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.5031 - accuracy: 0.8181 - val_loss: 0.4749 - val_accuracy: 0.8313\n",
      "Epoch 13/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5041 - accuracy: 0.8205 - val_loss: 0.5759 - val_accuracy: 0.7802\n",
      "Epoch 14/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.5018 - accuracy: 0.8189 - val_loss: 0.4948 - val_accuracy: 0.8228\n",
      "Epoch 15/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5018 - accuracy: 0.8208 - val_loss: 0.5221 - val_accuracy: 0.8057\n",
      "Epoch 16/250\n",
      "48000/48000 [==============================] - 2s 51us/sample - loss: 0.5036 - accuracy: 0.8180 - val_loss: 0.5100 - val_accuracy: 0.8112\n",
      "Epoch 17/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.5034 - accuracy: 0.8188 - val_loss: 0.4962 - val_accuracy: 0.8188\n",
      "Epoch 18/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5010 - accuracy: 0.8190 - val_loss: 0.4745 - val_accuracy: 0.8322\n",
      "Epoch 19/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5036 - accuracy: 0.8183 - val_loss: 0.5285 - val_accuracy: 0.7994\n",
      "Epoch 20/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.5022 - accuracy: 0.8191 - val_loss: 0.4968 - val_accuracy: 0.8169\n",
      "Epoch 21/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5006 - accuracy: 0.8203 - val_loss: 0.5234 - val_accuracy: 0.8118\n",
      "Epoch 22/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5015 - accuracy: 0.8185 - val_loss: 0.5176 - val_accuracy: 0.8091\n",
      "Epoch 23/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5008 - accuracy: 0.8191 - val_loss: 0.5353 - val_accuracy: 0.8033\n",
      "Epoch 24/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5003 - accuracy: 0.8209 - val_loss: 0.4900 - val_accuracy: 0.8242\n",
      "Epoch 25/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5003 - accuracy: 0.8204 - val_loss: 0.4831 - val_accuracy: 0.8288\n",
      "Epoch 26/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4993 - accuracy: 0.8187 - val_loss: 0.4959 - val_accuracy: 0.8253\n",
      "Epoch 27/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4960 - accuracy: 0.8223 - val_loss: 0.5028 - val_accuracy: 0.8203\n",
      "Epoch 28/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4988 - accuracy: 0.8213 - val_loss: 0.4918 - val_accuracy: 0.8185\n",
      "Epoch 29/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4996 - accuracy: 0.8200 - val_loss: 0.5271 - val_accuracy: 0.8117\n",
      "Epoch 30/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4976 - accuracy: 0.8210 - val_loss: 0.5234 - val_accuracy: 0.8083\n",
      "Epoch 31/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4990 - accuracy: 0.8198 - val_loss: 0.4834 - val_accuracy: 0.8272\n",
      "Epoch 32/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.5005 - accuracy: 0.8194 - val_loss: 0.5947 - val_accuracy: 0.7908\n",
      "Epoch 33/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4978 - accuracy: 0.8215 - val_loss: 0.4903 - val_accuracy: 0.8257\n",
      "Epoch 34/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4976 - accuracy: 0.8223 - val_loss: 0.4844 - val_accuracy: 0.8280\n",
      "Epoch 35/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4990 - accuracy: 0.8205 - val_loss: 0.5067 - val_accuracy: 0.8199\n",
      "Epoch 36/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4964 - accuracy: 0.8203 - val_loss: 0.5400 - val_accuracy: 0.7977\n",
      "Epoch 37/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4969 - accuracy: 0.8209 - val_loss: 0.4891 - val_accuracy: 0.8240\n",
      "Epoch 38/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4983 - accuracy: 0.8206 - val_loss: 0.4971 - val_accuracy: 0.8192\n",
      "Epoch 39/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4957 - accuracy: 0.8223 - val_loss: 0.5109 - val_accuracy: 0.8133\n",
      "Epoch 40/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4967 - accuracy: 0.8216 - val_loss: 0.5200 - val_accuracy: 0.8138\n",
      "Epoch 41/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4966 - accuracy: 0.8204 - val_loss: 0.5020 - val_accuracy: 0.8165\n",
      "Epoch 42/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4973 - accuracy: 0.8224 - val_loss: 0.5025 - val_accuracy: 0.8197\n",
      "Epoch 43/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4966 - accuracy: 0.8221 - val_loss: 0.4934 - val_accuracy: 0.8217\n",
      "Epoch 44/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4970 - accuracy: 0.8211 - val_loss: 0.5007 - val_accuracy: 0.8204\n",
      "Epoch 45/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4961 - accuracy: 0.8217 - val_loss: 0.4814 - val_accuracy: 0.8283\n",
      "Epoch 46/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4979 - accuracy: 0.8218 - val_loss: 0.4891 - val_accuracy: 0.8252\n",
      "Epoch 47/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4966 - accuracy: 0.8211 - val_loss: 0.5041 - val_accuracy: 0.8205\n",
      "Epoch 48/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4960 - accuracy: 0.8233 - val_loss: 0.4762 - val_accuracy: 0.8304\n",
      "Epoch 49/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4967 - accuracy: 0.8230 - val_loss: 0.5750 - val_accuracy: 0.7944\n",
      "Epoch 50/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4959 - accuracy: 0.8228 - val_loss: 0.4858 - val_accuracy: 0.8295\n",
      "Epoch 51/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4966 - accuracy: 0.8223 - val_loss: 0.4898 - val_accuracy: 0.8252\n",
      "Epoch 52/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4954 - accuracy: 0.8208 - val_loss: 0.5119 - val_accuracy: 0.8120\n",
      "Epoch 53/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4960 - accuracy: 0.8216 - val_loss: 0.4713 - val_accuracy: 0.8314\n",
      "Epoch 54/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4943 - accuracy: 0.8243 - val_loss: 0.4907 - val_accuracy: 0.8254\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4933 - accuracy: 0.8239 - val_loss: 0.5058 - val_accuracy: 0.8106\n",
      "Epoch 56/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4941 - accuracy: 0.8218 - val_loss: 0.4881 - val_accuracy: 0.8263\n",
      "Epoch 57/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4957 - accuracy: 0.8229 - val_loss: 0.6258 - val_accuracy: 0.7685\n",
      "Epoch 58/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4945 - accuracy: 0.8240 - val_loss: 0.5444 - val_accuracy: 0.7958\n",
      "Epoch 59/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4931 - accuracy: 0.8236 - val_loss: 0.4744 - val_accuracy: 0.8308\n",
      "Epoch 60/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4956 - accuracy: 0.8227 - val_loss: 0.5308 - val_accuracy: 0.7999\n",
      "Epoch 61/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4916 - accuracy: 0.8230 - val_loss: 0.5000 - val_accuracy: 0.8217\n",
      "Epoch 62/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4939 - accuracy: 0.8231 - val_loss: 0.4781 - val_accuracy: 0.8285\n",
      "Epoch 63/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4917 - accuracy: 0.8236 - val_loss: 0.5165 - val_accuracy: 0.8158\n",
      "Epoch 64/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4943 - accuracy: 0.8235 - val_loss: 0.4865 - val_accuracy: 0.8253\n",
      "Epoch 65/250\n",
      "48000/48000 [==============================] - 3s 71us/sample - loss: 0.4913 - accuracy: 0.8245 - val_loss: 0.4880 - val_accuracy: 0.8248\n",
      "Epoch 66/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4944 - accuracy: 0.8224 - val_loss: 0.5546 - val_accuracy: 0.8037\n",
      "Epoch 67/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4916 - accuracy: 0.8234 - val_loss: 0.6179 - val_accuracy: 0.7795\n",
      "Epoch 68/250\n",
      "48000/48000 [==============================] - 4s 77us/sample - loss: 0.4903 - accuracy: 0.8248 - val_loss: 0.5114 - val_accuracy: 0.8102\n",
      "Epoch 69/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4923 - accuracy: 0.8230 - val_loss: 0.4833 - val_accuracy: 0.8259\n",
      "Epoch 70/250\n",
      "48000/48000 [==============================] - 4s 74us/sample - loss: 0.4932 - accuracy: 0.8224 - val_loss: 0.4750 - val_accuracy: 0.8317\n",
      "Epoch 71/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4933 - accuracy: 0.8219 - val_loss: 0.4943 - val_accuracy: 0.8238\n",
      "Epoch 72/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4919 - accuracy: 0.8227 - val_loss: 0.5687 - val_accuracy: 0.7759\n",
      "Epoch 73/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4907 - accuracy: 0.8245 - val_loss: 0.4758 - val_accuracy: 0.8317\n",
      "Epoch 74/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4917 - accuracy: 0.8247 - val_loss: 0.4827 - val_accuracy: 0.8270\n",
      "Epoch 75/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4920 - accuracy: 0.8236 - val_loss: 0.4986 - val_accuracy: 0.8231\n",
      "Epoch 76/250\n",
      "48000/48000 [==============================] - 4s 77us/sample - loss: 0.4907 - accuracy: 0.8247 - val_loss: 0.4771 - val_accuracy: 0.8274\n",
      "Epoch 77/250\n",
      "48000/48000 [==============================] - 4s 77us/sample - loss: 0.4919 - accuracy: 0.8239 - val_loss: 0.4808 - val_accuracy: 0.8273\n",
      "Epoch 78/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4912 - accuracy: 0.8228 - val_loss: 0.4864 - val_accuracy: 0.8270\n",
      "Epoch 79/250\n",
      "48000/48000 [==============================] - 4s 77us/sample - loss: 0.4916 - accuracy: 0.8238 - val_loss: 0.4763 - val_accuracy: 0.8282\n",
      "Epoch 80/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4893 - accuracy: 0.8252 - val_loss: 0.5174 - val_accuracy: 0.8155\n",
      "Epoch 81/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4923 - accuracy: 0.8222 - val_loss: 0.4988 - val_accuracy: 0.8192\n",
      "Epoch 82/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4884 - accuracy: 0.8261 - val_loss: 0.4691 - val_accuracy: 0.8315\n",
      "Epoch 83/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4881 - accuracy: 0.8255 - val_loss: 0.4738 - val_accuracy: 0.8306\n",
      "Epoch 84/250\n",
      "48000/48000 [==============================] - 4s 77us/sample - loss: 0.4915 - accuracy: 0.8249 - val_loss: 0.5126 - val_accuracy: 0.8133\n",
      "Epoch 85/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4888 - accuracy: 0.8258 - val_loss: 0.4991 - val_accuracy: 0.8225\n",
      "Epoch 86/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4904 - accuracy: 0.8247 - val_loss: 0.4927 - val_accuracy: 0.8217\n",
      "Epoch 87/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4883 - accuracy: 0.8239 - val_loss: 0.4674 - val_accuracy: 0.8329\n",
      "Epoch 88/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4909 - accuracy: 0.8226 - val_loss: 0.4676 - val_accuracy: 0.8301\n",
      "Epoch 89/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4903 - accuracy: 0.8250 - val_loss: 0.4818 - val_accuracy: 0.8265\n",
      "Epoch 90/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4901 - accuracy: 0.8232 - val_loss: 0.4939 - val_accuracy: 0.8204\n",
      "Epoch 91/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4871 - accuracy: 0.8261 - val_loss: 0.4819 - val_accuracy: 0.8267\n",
      "Epoch 92/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4870 - accuracy: 0.8248 - val_loss: 0.4845 - val_accuracy: 0.8272\n",
      "Epoch 93/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4875 - accuracy: 0.8257 - val_loss: 0.4913 - val_accuracy: 0.8205\n",
      "Epoch 94/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4875 - accuracy: 0.8257 - val_loss: 0.4903 - val_accuracy: 0.8267\n",
      "Epoch 95/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4881 - accuracy: 0.8248 - val_loss: 0.4758 - val_accuracy: 0.8285\n",
      "Epoch 96/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4889 - accuracy: 0.8235 - val_loss: 0.5553 - val_accuracy: 0.7980\n",
      "Epoch 97/250\n",
      "48000/48000 [==============================] - 4s 77us/sample - loss: 0.4892 - accuracy: 0.8243 - val_loss: 0.4947 - val_accuracy: 0.8220\n",
      "Epoch 98/250\n",
      "48000/48000 [==============================] - 4s 77us/sample - loss: 0.4923 - accuracy: 0.8232 - val_loss: 0.4928 - val_accuracy: 0.8288\n",
      "Epoch 99/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4879 - accuracy: 0.8275 - val_loss: 0.5018 - val_accuracy: 0.8202\n",
      "Epoch 100/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4889 - accuracy: 0.8253 - val_loss: 0.4930 - val_accuracy: 0.8248\n",
      "Epoch 101/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4870 - accuracy: 0.8253 - val_loss: 0.5019 - val_accuracy: 0.8186\n",
      "Epoch 102/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4881 - accuracy: 0.8264 - val_loss: 0.5017 - val_accuracy: 0.8187\n",
      "Epoch 103/250\n",
      "48000/48000 [==============================] - 4s 75us/sample - loss: 0.4854 - accuracy: 0.8248 - val_loss: 0.5160 - val_accuracy: 0.8114\n",
      "Epoch 104/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4857 - accuracy: 0.8265 - val_loss: 0.4771 - val_accuracy: 0.8280\n",
      "Epoch 105/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4877 - accuracy: 0.8257 - val_loss: 0.5184 - val_accuracy: 0.8119\n",
      "Epoch 106/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4864 - accuracy: 0.8263 - val_loss: 0.4754 - val_accuracy: 0.8269\n",
      "Epoch 107/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4863 - accuracy: 0.8259 - val_loss: 0.4972 - val_accuracy: 0.8213\n",
      "Epoch 108/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4884 - accuracy: 0.8256 - val_loss: 0.4703 - val_accuracy: 0.8309\n",
      "Epoch 109/250\n",
      "48000/48000 [==============================] - 4s 76us/sample - loss: 0.4859 - accuracy: 0.8261 - val_loss: 0.4795 - val_accuracy: 0.8298\n",
      "Epoch 110/250\n",
      "48000/48000 [==============================] - 3s 71us/sample - loss: 0.4842 - accuracy: 0.8272 - val_loss: 0.5113 - val_accuracy: 0.8168\n",
      "Epoch 111/250\n",
      "48000/48000 [==============================] - 3s 70us/sample - loss: 0.4849 - accuracy: 0.8261 - val_loss: 0.5071 - val_accuracy: 0.8178\n",
      "Epoch 112/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4850 - accuracy: 0.8264 - val_loss: 0.4931 - val_accuracy: 0.8233\n",
      "Epoch 113/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4840 - accuracy: 0.8276 - val_loss: 0.4707 - val_accuracy: 0.8318\n",
      "Epoch 114/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4847 - accuracy: 0.8276 - val_loss: 0.5039 - val_accuracy: 0.8151\n",
      "Epoch 115/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4856 - accuracy: 0.8269 - val_loss: 0.4752 - val_accuracy: 0.8278\n",
      "Epoch 116/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4846 - accuracy: 0.8268 - val_loss: 0.5165 - val_accuracy: 0.8150\n",
      "Epoch 117/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4861 - accuracy: 0.8274 - val_loss: 0.4688 - val_accuracy: 0.8322\n",
      "Epoch 118/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4846 - accuracy: 0.8253 - val_loss: 0.4896 - val_accuracy: 0.8223\n",
      "Epoch 119/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4845 - accuracy: 0.8262 - val_loss: 0.4748 - val_accuracy: 0.8297\n",
      "Epoch 120/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4850 - accuracy: 0.8259 - val_loss: 0.5025 - val_accuracy: 0.8178\n",
      "Epoch 121/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4834 - accuracy: 0.8267 - val_loss: 0.4876 - val_accuracy: 0.8258\n",
      "Epoch 122/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4832 - accuracy: 0.8284 - val_loss: 0.4615 - val_accuracy: 0.8338\n",
      "Epoch 123/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4856 - accuracy: 0.8254 - val_loss: 0.4896 - val_accuracy: 0.8270\n",
      "Epoch 124/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4838 - accuracy: 0.8259 - val_loss: 0.4628 - val_accuracy: 0.8347\n",
      "Epoch 125/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4839 - accuracy: 0.8264 - val_loss: 0.4838 - val_accuracy: 0.8288\n",
      "Epoch 126/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4856 - accuracy: 0.8262 - val_loss: 0.4790 - val_accuracy: 0.8250\n",
      "Epoch 127/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4840 - accuracy: 0.8274 - val_loss: 0.4811 - val_accuracy: 0.8278\n",
      "Epoch 128/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4811 - accuracy: 0.8284 - val_loss: 0.5032 - val_accuracy: 0.8171\n",
      "Epoch 129/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4854 - accuracy: 0.8264 - val_loss: 0.4653 - val_accuracy: 0.8333\n",
      "Epoch 130/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4812 - accuracy: 0.8279 - val_loss: 0.4802 - val_accuracy: 0.8297\n",
      "Epoch 131/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4832 - accuracy: 0.8272 - val_loss: 0.4733 - val_accuracy: 0.8296\n",
      "Epoch 132/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4826 - accuracy: 0.8275 - val_loss: 0.5335 - val_accuracy: 0.7970\n",
      "Epoch 133/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4841 - accuracy: 0.8254 - val_loss: 0.5066 - val_accuracy: 0.8213\n",
      "Epoch 134/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4826 - accuracy: 0.8259 - val_loss: 0.5072 - val_accuracy: 0.8092\n",
      "Epoch 135/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4798 - accuracy: 0.8274 - val_loss: 0.5805 - val_accuracy: 0.7952\n",
      "Epoch 136/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4812 - accuracy: 0.8269 - val_loss: 0.5246 - val_accuracy: 0.8142\n",
      "Epoch 137/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4834 - accuracy: 0.8260 - val_loss: 0.5070 - val_accuracy: 0.8116\n",
      "Epoch 138/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4804 - accuracy: 0.8275 - val_loss: 0.5015 - val_accuracy: 0.8199\n",
      "Epoch 139/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4808 - accuracy: 0.8286 - val_loss: 0.5413 - val_accuracy: 0.7943\n",
      "Epoch 140/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4813 - accuracy: 0.8289 - val_loss: 0.4883 - val_accuracy: 0.8255\n",
      "Epoch 141/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4830 - accuracy: 0.8261 - val_loss: 0.4807 - val_accuracy: 0.8217\n",
      "Epoch 142/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4806 - accuracy: 0.8297 - val_loss: 0.5090 - val_accuracy: 0.8103\n",
      "Epoch 143/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4819 - accuracy: 0.8277 - val_loss: 0.4748 - val_accuracy: 0.8317\n",
      "Epoch 144/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4815 - accuracy: 0.8276 - val_loss: 0.4915 - val_accuracy: 0.8223\n",
      "Epoch 145/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4815 - accuracy: 0.8276 - val_loss: 0.5341 - val_accuracy: 0.7974\n",
      "Epoch 146/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4791 - accuracy: 0.8295 - val_loss: 0.4894 - val_accuracy: 0.8233\n",
      "Epoch 147/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4809 - accuracy: 0.8280 - val_loss: 0.4590 - val_accuracy: 0.8368\n",
      "Epoch 148/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4795 - accuracy: 0.8283 - val_loss: 0.4908 - val_accuracy: 0.8260\n",
      "Epoch 149/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4803 - accuracy: 0.8272 - val_loss: 0.5045 - val_accuracy: 0.8125\n",
      "Epoch 150/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4784 - accuracy: 0.8288 - val_loss: 0.4858 - val_accuracy: 0.8295\n",
      "Epoch 151/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4794 - accuracy: 0.8278 - val_loss: 0.4654 - val_accuracy: 0.8319\n",
      "Epoch 152/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4785 - accuracy: 0.8288 - val_loss: 0.5410 - val_accuracy: 0.8063\n",
      "Epoch 153/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4767 - accuracy: 0.8296 - val_loss: 0.5294 - val_accuracy: 0.8027\n",
      "Epoch 154/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4797 - accuracy: 0.8279 - val_loss: 0.4613 - val_accuracy: 0.8337\n",
      "Epoch 155/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4781 - accuracy: 0.8277 - val_loss: 0.4887 - val_accuracy: 0.8223\n",
      "Epoch 156/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4802 - accuracy: 0.8275 - val_loss: 0.4860 - val_accuracy: 0.8226\n",
      "Epoch 157/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4791 - accuracy: 0.8281 - val_loss: 0.4835 - val_accuracy: 0.8288\n",
      "Epoch 158/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4783 - accuracy: 0.8304 - val_loss: 0.4632 - val_accuracy: 0.8330\n",
      "Epoch 159/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4787 - accuracy: 0.8295 - val_loss: 0.4947 - val_accuracy: 0.8180\n",
      "Epoch 160/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4775 - accuracy: 0.8295 - val_loss: 0.5253 - val_accuracy: 0.8075\n",
      "Epoch 161/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4794 - accuracy: 0.8290 - val_loss: 0.4828 - val_accuracy: 0.8288\n",
      "Epoch 162/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4758 - accuracy: 0.8307 - val_loss: 0.5132 - val_accuracy: 0.8092\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 163/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4780 - accuracy: 0.8291 - val_loss: 0.4793 - val_accuracy: 0.8259\n",
      "Epoch 164/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4782 - accuracy: 0.8294 - val_loss: 0.4603 - val_accuracy: 0.8356\n",
      "Epoch 165/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4776 - accuracy: 0.8287 - val_loss: 0.5075 - val_accuracy: 0.8183\n",
      "Epoch 166/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4771 - accuracy: 0.8294 - val_loss: 0.4576 - val_accuracy: 0.8360\n",
      "Epoch 167/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4788 - accuracy: 0.8298 - val_loss: 0.4847 - val_accuracy: 0.8206\n",
      "Epoch 168/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4779 - accuracy: 0.8291 - val_loss: 0.4637 - val_accuracy: 0.8339\n",
      "Epoch 169/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4783 - accuracy: 0.8283 - val_loss: 0.5031 - val_accuracy: 0.8196\n",
      "Epoch 170/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4778 - accuracy: 0.8307 - val_loss: 0.4882 - val_accuracy: 0.8222\n",
      "Epoch 171/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4772 - accuracy: 0.8287 - val_loss: 0.4791 - val_accuracy: 0.8211\n",
      "Epoch 172/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4753 - accuracy: 0.8304 - val_loss: 0.4951 - val_accuracy: 0.8225\n",
      "Epoch 173/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4779 - accuracy: 0.8288 - val_loss: 0.4915 - val_accuracy: 0.8249\n",
      "Epoch 174/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4774 - accuracy: 0.8291 - val_loss: 0.4662 - val_accuracy: 0.8337\n",
      "Epoch 175/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4762 - accuracy: 0.8290 - val_loss: 0.4680 - val_accuracy: 0.8348\n",
      "Epoch 176/250\n",
      "48000/48000 [==============================] - 2s 50us/sample - loss: 0.4784 - accuracy: 0.8298 - val_loss: 0.4629 - val_accuracy: 0.8322\n",
      "Epoch 177/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4772 - accuracy: 0.8298 - val_loss: 0.4645 - val_accuracy: 0.8353\n",
      "Epoch 178/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4770 - accuracy: 0.8303 - val_loss: 0.4909 - val_accuracy: 0.8232\n",
      "Epoch 179/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4770 - accuracy: 0.8289 - val_loss: 0.4692 - val_accuracy: 0.8292\n",
      "Epoch 180/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4768 - accuracy: 0.8282 - val_loss: 0.4701 - val_accuracy: 0.8322\n",
      "Epoch 181/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4757 - accuracy: 0.8306 - val_loss: 0.4877 - val_accuracy: 0.8225\n",
      "Epoch 182/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4752 - accuracy: 0.8302 - val_loss: 0.4802 - val_accuracy: 0.8294\n",
      "Epoch 183/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4754 - accuracy: 0.8291 - val_loss: 0.4798 - val_accuracy: 0.8291\n",
      "Epoch 184/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4739 - accuracy: 0.8313 - val_loss: 0.4943 - val_accuracy: 0.8144\n",
      "Epoch 185/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4751 - accuracy: 0.8303 - val_loss: 0.4590 - val_accuracy: 0.8353\n",
      "Epoch 186/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4745 - accuracy: 0.8317 - val_loss: 0.4643 - val_accuracy: 0.8351\n",
      "Epoch 187/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4754 - accuracy: 0.8283 - val_loss: 0.4875 - val_accuracy: 0.8267\n",
      "Epoch 188/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4764 - accuracy: 0.8292 - val_loss: 0.5064 - val_accuracy: 0.8149\n",
      "Epoch 189/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4744 - accuracy: 0.8307 - val_loss: 0.4779 - val_accuracy: 0.8271\n",
      "Epoch 190/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4750 - accuracy: 0.8304 - val_loss: 0.4962 - val_accuracy: 0.8251\n",
      "Epoch 191/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4753 - accuracy: 0.8302 - val_loss: 0.4744 - val_accuracy: 0.8303\n",
      "Epoch 192/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4746 - accuracy: 0.8323 - val_loss: 0.5430 - val_accuracy: 0.8060\n",
      "Epoch 193/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4752 - accuracy: 0.8300 - val_loss: 0.5093 - val_accuracy: 0.8122\n",
      "Epoch 194/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4753 - accuracy: 0.8327 - val_loss: 0.4621 - val_accuracy: 0.8363\n",
      "Epoch 195/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4744 - accuracy: 0.8317 - val_loss: 0.5150 - val_accuracy: 0.8147\n",
      "Epoch 196/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4735 - accuracy: 0.8304 - val_loss: 0.4753 - val_accuracy: 0.8270\n",
      "Epoch 197/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4733 - accuracy: 0.8305 - val_loss: 0.4799 - val_accuracy: 0.8262\n",
      "Epoch 198/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4747 - accuracy: 0.8307 - val_loss: 0.4635 - val_accuracy: 0.8336\n",
      "Epoch 199/250\n",
      "48000/48000 [==============================] - 2s 46us/sample - loss: 0.4722 - accuracy: 0.8307 - val_loss: 0.4862 - val_accuracy: 0.8309\n",
      "Epoch 200/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4728 - accuracy: 0.8302 - val_loss: 0.4790 - val_accuracy: 0.8320\n",
      "Epoch 201/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4717 - accuracy: 0.8323 - val_loss: 0.4706 - val_accuracy: 0.8288\n",
      "Epoch 202/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4733 - accuracy: 0.8304 - val_loss: 0.4695 - val_accuracy: 0.8356\n",
      "Epoch 203/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4753 - accuracy: 0.8297 - val_loss: 0.4597 - val_accuracy: 0.8346\n",
      "Epoch 204/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4725 - accuracy: 0.8315 - val_loss: 0.4707 - val_accuracy: 0.8326\n",
      "Epoch 205/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4737 - accuracy: 0.8321 - val_loss: 0.5299 - val_accuracy: 0.8031\n",
      "Epoch 206/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4714 - accuracy: 0.8310 - val_loss: 0.4617 - val_accuracy: 0.8318\n",
      "Epoch 207/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4737 - accuracy: 0.8312 - val_loss: 0.4663 - val_accuracy: 0.8288\n",
      "Epoch 208/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4723 - accuracy: 0.8307 - val_loss: 0.5093 - val_accuracy: 0.8141\n",
      "Epoch 209/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4723 - accuracy: 0.8304 - val_loss: 0.4872 - val_accuracy: 0.8255\n",
      "Epoch 210/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4722 - accuracy: 0.8311 - val_loss: 0.5000 - val_accuracy: 0.8214\n",
      "Epoch 211/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4743 - accuracy: 0.8309 - val_loss: 0.4583 - val_accuracy: 0.8367\n",
      "Epoch 212/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4709 - accuracy: 0.8316 - val_loss: 0.4870 - val_accuracy: 0.8227\n",
      "Epoch 213/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4704 - accuracy: 0.8316 - val_loss: 0.5200 - val_accuracy: 0.8069\n",
      "Epoch 214/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4714 - accuracy: 0.8317 - val_loss: 0.4713 - val_accuracy: 0.8262\n",
      "Epoch 215/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4703 - accuracy: 0.8311 - val_loss: 0.5557 - val_accuracy: 0.7993\n",
      "Epoch 216/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4712 - accuracy: 0.8312 - val_loss: 0.4543 - val_accuracy: 0.8393\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 217/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4696 - accuracy: 0.8320 - val_loss: 0.4545 - val_accuracy: 0.8396\n",
      "Epoch 218/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4710 - accuracy: 0.8322 - val_loss: 0.4813 - val_accuracy: 0.8269\n",
      "Epoch 219/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4708 - accuracy: 0.8317 - val_loss: 0.4931 - val_accuracy: 0.8219\n",
      "Epoch 220/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4697 - accuracy: 0.8322 - val_loss: 0.4599 - val_accuracy: 0.8377\n",
      "Epoch 221/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4710 - accuracy: 0.8322 - val_loss: 0.4601 - val_accuracy: 0.8359\n",
      "Epoch 222/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4713 - accuracy: 0.8313 - val_loss: 0.4694 - val_accuracy: 0.8312\n",
      "Epoch 223/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4712 - accuracy: 0.8315 - val_loss: 0.4579 - val_accuracy: 0.8374\n",
      "Epoch 224/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4700 - accuracy: 0.8316 - val_loss: 0.4595 - val_accuracy: 0.8378\n",
      "Epoch 225/250\n",
      "48000/48000 [==============================] - 2s 46us/sample - loss: 0.4712 - accuracy: 0.8319 - val_loss: 0.4600 - val_accuracy: 0.8345\n",
      "Epoch 226/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4700 - accuracy: 0.8314 - val_loss: 0.5098 - val_accuracy: 0.8168\n",
      "Epoch 227/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4706 - accuracy: 0.8315 - val_loss: 0.4748 - val_accuracy: 0.8311\n",
      "Epoch 228/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4712 - accuracy: 0.8294 - val_loss: 0.4777 - val_accuracy: 0.8315\n",
      "Epoch 229/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4686 - accuracy: 0.8327 - val_loss: 0.5582 - val_accuracy: 0.7971\n",
      "Epoch 230/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4686 - accuracy: 0.8328 - val_loss: 0.5014 - val_accuracy: 0.8161\n",
      "Epoch 231/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4698 - accuracy: 0.8330 - val_loss: 0.4691 - val_accuracy: 0.8296\n",
      "Epoch 232/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4693 - accuracy: 0.8326 - val_loss: 0.5167 - val_accuracy: 0.8138\n",
      "Epoch 233/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4684 - accuracy: 0.8325 - val_loss: 0.4813 - val_accuracy: 0.8271\n",
      "Epoch 234/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4705 - accuracy: 0.8316 - val_loss: 0.4947 - val_accuracy: 0.8236\n",
      "Epoch 235/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4698 - accuracy: 0.8334 - val_loss: 0.4700 - val_accuracy: 0.8334\n",
      "Epoch 236/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4687 - accuracy: 0.8328 - val_loss: 0.4698 - val_accuracy: 0.8320\n",
      "Epoch 237/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4704 - accuracy: 0.8309 - val_loss: 0.4835 - val_accuracy: 0.8232\n",
      "Epoch 238/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4691 - accuracy: 0.8329 - val_loss: 0.4988 - val_accuracy: 0.8128\n",
      "Epoch 239/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4670 - accuracy: 0.8336 - val_loss: 0.4644 - val_accuracy: 0.8328\n",
      "Epoch 240/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4704 - accuracy: 0.8326 - val_loss: 0.4846 - val_accuracy: 0.8281\n",
      "Epoch 241/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4675 - accuracy: 0.8314 - val_loss: 0.4651 - val_accuracy: 0.8336\n",
      "Epoch 242/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4685 - accuracy: 0.8335 - val_loss: 0.4593 - val_accuracy: 0.8361\n",
      "Epoch 243/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4660 - accuracy: 0.8324 - val_loss: 0.4798 - val_accuracy: 0.8260\n",
      "Epoch 244/250\n",
      "48000/48000 [==============================] - 2s 49us/sample - loss: 0.4682 - accuracy: 0.8327 - val_loss: 0.4950 - val_accuracy: 0.8192\n",
      "Epoch 245/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4685 - accuracy: 0.8312 - val_loss: 0.5230 - val_accuracy: 0.8085\n",
      "Epoch 246/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4680 - accuracy: 0.8335 - val_loss: 0.4564 - val_accuracy: 0.8380\n",
      "Epoch 247/250\n",
      "48000/48000 [==============================] - 2s 47us/sample - loss: 0.4673 - accuracy: 0.8333 - val_loss: 0.4627 - val_accuracy: 0.8303\n",
      "Epoch 248/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4646 - accuracy: 0.8338 - val_loss: 0.5002 - val_accuracy: 0.8218\n",
      "Epoch 249/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4676 - accuracy: 0.8320 - val_loss: 0.4844 - val_accuracy: 0.8259\n",
      "Epoch 250/250\n",
      "48000/48000 [==============================] - 2s 48us/sample - loss: 0.4691 - accuracy: 0.8327 - val_loss: 0.4572 - val_accuracy: 0.8375\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "history = model.fit(x=x_train,y=y_train,batch_size=32,epochs=250,validation_split=0.2,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WYvDhLxGuKsVyxOGQlAVJ2Tg+LET+4S+ETniChu0fYEvZiAz6CXoscNkremclEGD7JZeSe+PPST39Rv03k5Lg5hUEXvshedddTmhPBTX/e5HwhB9iKfg1weJG4EnCXh+VX/pwjx+D+54/YW1ywX8/pinjXJxnXALH30n4+9mEX3uCtDPPpvrF/9HweSGpp5xGcMaf0Hyn4T7rOhh8JGwtjO892EvUGHUceL//HgDZ1HHvyb9BF+pQTU2n59Rqa9l59TXsvOJKgu2M8xh4vv1WF4MITcuWsfUHZ+BZsqTT63SEb9MmNh99DJX/ag7a8XyzxCyfVl+Pd/lyyh98EOHR3U2BiMvLv2ED3uXL2z23VhdpUKuay1355D/Zfs65aPX1cZWvbs4cwo2NWHNyaJg3z9xuRGE2fDzXjAz2LFnC7j/8gcqnnqLmtdep+MeTbJ45Sx+DiuCNRJ/m3fJr/V5ajOc1RX7n3F/eTPZ111H39tvUf9Ic2RooLqZ+7lyz3qWU1M55h62nnErJTTeb+8lgkOIbbqDs9tvhs/tgz4Y2709qGtsvucSMng1VVRGqrkZqGp5vviX9vPNImj4dz4IvYcUruuUAaLXVNH37LQD+d/9MeM7NFF12Nt5PXiGw8K3IySWeb7+l5rVX8a1bR2jXLpCShvffxJ2rkTmoBKTENWECaaedhnva4VQ+9TShij3U/fMedl86Dfn4JAKL38c5OJchl/Yj92g3gjDe95+Geffq44MGW+fDp/fAqjegcU/ECnwZ/n44vHwBfHIHfHwbzPkZvP1TCGsEi/Txysb//RFeuQSeOgb5zX/wVQRwBVbgTi7HO/89+O+pBP4yi2BFLZkjdest9O7vYeciGDydULn+/ng/fA5eugBeuZSKe3+OPSVE5omTCHicyAkXweRLCViHUzKngjrnhXDTcpr8ekcqdVw6gYpGApvWmK1iXUPz0Ir/k/9A/kS46n2wOXVr7tJXkPmT8KzeSUr/AGLXcqjZAZMugjMew3XRHfr95VxF9aow6cOayBhajbfSgff0OdTvTCLsyKV+mx3P+PsI9jsZ4XJhvXEuqb/U80k1vvEM+BsIn/53mma9gLytiMqycXod9D8RDr0MfvEdIXIACJZXwszf4D3jAwI/+o6G8jRSz7kE+50r6ffmGtKe3sGA2QtxTZxI/ennIG7fAhf/D370HtZBIwk1BpBJWWiNAdyHHUZgTwP1SefBL76D3xXBzxfhT5kONhvJM2bi3+3RBWvAVN3qHns6nHAX4ZAN64jDIXskrgkTCFVUEKwP4N9ZgnPkSL1SrTbcp19N8hV3wdjT9PNkj8Qxerz+vjnH4S+rI+nYk7APGICWOQUyh+m/x9at+NevNwO9/Nu2Edi5k6b1RQAkH3UUKbNmIYMhPFkXQu4Yc2jBGE7IuuEXiPxDcAwbhi0vz3ynSMpCG657C+35+aTMnGnGaZjj01MObfOd7ikOeovat2kT1k6EsDOMBlzzeDrcz7cuYlHHIdSVTz1FuKEB4XJReuttDH3xhVbuTdBd0UU//gk51/2U3JtvpqGwkNKbf4kMBKh5/XWSjzqqG3ekU//xx8hgkIq//hXn2DGkHHccTZGpEVptHVpNbeTvWlLeex/OOINgSSnYbFiSkqh5+WWSDj+cUEUFZbf/jgEP/xlbTo55jF4XemMuNY26OXOQwSC+detJPmo6geJirBkZWFNTW5VNhsPUvPQS7ilTSDnpRCoefYxgWRn2AQMIe5uwDxpE2OOh/MEHGfyvpym95Td65yAqcMiakYFnyRJk/W5Ear4erSkESZWzsWakENi4Bqq2woYPIGcs3uXfY83IwDFsGLm/uBHPokXsvvdekmfMILDia3Zc90sIAzYbo//2U5q+X8uufy8AmzUmcrpx4UK0ykq0ykqC8+Zj/+5ZOPMxSO3H6E1Pw5rbYNpPCDY58K1cheeZ20nzvU/pP75Ca9Lof8E4wvX1JGdV4t+zlaotFYTfugGLzQLZo/CsqgDNDQIC5XX4vnwXz/dJOJtux5UVBHQrsOovvydUpv8O3qeuRVp34N8uyZuVTnq/cipW2EgfnwSvXEr/YSvZvlxSdO4s/FUAgqwLb8L/zpukpa7AWRXAedw0alZtQtu5BhYthHXvwpE/g6JFeoAOApAgLLr1V1ekN+AnPU944NF433qcpDVPIwRw6SuECv8C7MazrQFZV4pIyScw6Vbk7AdxzTwXe00tjW8vJVRRTqBaDwpKPuMyap54j9DE6+Cme8FqR/vn0UAtvvIQsrEKX1kDvgoL+dddhHX0dJj3WwLjf45z9GgCX38NXEvAORayR+LxDscxyk3S+ZfR8OCDNDUNIPmoMXiWfEPThjIsKSkImxV/3ky4+nFwJMNNy/R7FAJv0wC0wA9J+fljcPrpMc+wXUqs98+m8q35SA2yH3qB4LpvqX7oOXb/6a9gszHklbfYctLJ+KvD+vPdvz9CCOyjJuIcM5JGnGTf9CaVTzxB1dNX4po4kdAuPSI6WF6uvyuZwwlWVOrbdpURLN/Djosv1jtLEOv9sDmw5eQx/M032FlYqFux43VRsmX9D62qinBDAzIYJPWkk/Bv2IB/kz784du4EVtWFv4tW3AMG4pr4kQav/qKsNeLxe2m5rXXkCGNzMt/iNagB5MBuCdPBqBuztuE6+pwjhrZRmvUjGOo7iHxrVtPsKSEjAsvQGtqQovcIzTHMQS26h6QsltvI1hejmviBKwZGTjHjAFNw5KcTGPhl6SeeCLByDs68LFHaVz4NaknngiAEIKkI4/U2wopEUKYBoY1K5vUE46n/oMP8K5cSdP332NJTu70HhLNQW1Ry0CAoquuJuXdlmuIdA3v9ysAfaJ+e4Q9HtNCMwSuPQI7dlD98itkXHgh/f/vPrzLlzcHoLQgWFICoZD54FT/91ls+fmknnYani8XmBHKUkoaCguRwWDc99Xw6TzcU6fiPGQcZbf/Dt/KlYQi0yi0ujq0Ov0+HCNH4l6wQO8VlxRjHzCA9JOPpX7uXMJfPYnni490F+369RAKwPoP0Ir0KE8t4gnwLFpsntu34ju8S75g68mnsOmIIyk6twACEW/F7jXwyiUE/3EmgZ07ST/vXFJPOkkv72efg5TI3VuwhSvIPzKId+VKdlxwNlpNNcN/NICxz9/KqE/eY9T8L8i99oeE6+oI3j8ePvs93sWf40wLYFn/Jg57FYEl78PfD9Otw1cvwTv/HdyDXIiy5QirlX4nZaHV1VH39zuoeeQ3WCxhBs6ohlCI+v/8iZp35mFPDpE9uhatqhK57n1Y/iJ1Tz+EcOju9kZtGjU7s9nx898inz2D/rvm6RbZ3Nvxv/Y7APw1Ern2PXy7A/grQ+z5QK+75Ko3caV5QAr8x/0Ljv015IyhsWkU1tQkkqZPJ2AbjW/q/Xq9OqYSGHEFCIF7eAa+olqERSKsAu/mYhqq+gOQetdsbNe8xKhzqsm0vAfV23BOPZbc08birxI4Bugu/IaGUYT9AufUArjxG7j6A2xDJ6ENPgV+PBeCPt1S3r4Ajvsd3FkKP1sAs26DrOFw3r/g2i9gwnk0LllB0SPvUeu+HE68Fzn2BwQr67FmZqI1hfGf8F/48Vz8Xr2T4Trz57jP070UvqOfIDBR/9t96e+QQhCy5ILVjpQSrd6DLT+fcCCMv+BpauUPEG436df+FueoUXodR8baDe+V0Wj71qwh6bDDzXFwraoK5+jROAYP1j0O48bhHDMW3646XaRBH+sWAqlpVL/4IlitJB9zTKv3SwiBa9JEpM9H8owZOA8vIOn8G/UgszVrSJk1C/vAgdgHD8a/abPZETVImn4M3k3bI5bcSizJyfjWrME5/hBsubmEIslBtJoaCAaxpKURKt+D9/vlEA6TcdFFZF5xBUlHTOusKQDAmp1FqLqaUGUVALbcHByjR+HftAkZDlN09TUU/exn+DdtwjlqNM6xYyAcxr9lCwA1r75G7RtvEPY0gaZhTdWF2jVlCq6JE6l88p8AOEZ0LHK2fv0QDgcNn30GgHPMWKzp6TFDir6I99K/dSvhQADfpk1oVVV4vlxA0vTpCIsFYbeTPGOGbg1LSaCkBGtODrbcXDLOOxcRFSSXMmsmWmWlGWkfqtI7BbbsLJJnzgSbjZpXXqXuvfdJnjmzzfidnuSgtqiFw0HGRRcR+ve/CZSUdimNXbipiYon/kb6+eeZvbawRxeTxq8WEm6oj+mp+jZu0oNUhOjUom6YXwihEDk/vwFbfj61b7xJ5b+eJmP6UCyjj42JsgwU6a4cw5Ws1dbgGjOKjGPH0TB3Lk2LF5Ny3HH4Vq+m5PobyL3lFnKu+6keDWpz6gEroJdty+d6ZGb2aPx1gsDWreTffTdJRxzB9nPPpfSW35jX1T57FG2rbh1nTM1mz9ateNes1esxL4OkyjnUhJ0E3rwX/65MwE74u1fgm59AYznahkzATejzv0PaYupe/A6LU2CxSnxvP0L4sxCQSnJ/P54Nu5H/PhmRlg9bvwBXGv5iC+DGuetdnJUeHIP74/n8fbKs7xPetRlLcjJp04ZQt2YlntIKMsYEcSVVwdxbsNhckDMa19oNQC4+6yTsC5/At64fKaNS4XfrcFT/msavl8Apt9BYPwC5ez2B2pdIH7YV/n0CZI3AXb0NV+4Aqt/6mJDXSvppx5N29UlUFT9OVXE2oYoqcm+6HkvVBlhfSPh/VyIRNKzOJ2u0h4aSJOqKM/BvqSHscdI49j5W5Q5nxsnnwNo5BN6cC3xFwOMmdPXHhF/Qg7+aSiXO0aOw3b8M155q+PhkfJUC96X3Eg4EaPzrcSQXnIg1LY26d94xPTm+7buwDRmDvX9/Us69GO/jfyXlhBMJVVThtdqgSuIcl4Zj6DBgGLa7Nurjxk49RiHrXA37KfNIPvpotpx0MrVv6FNWHKdcB5m6lWPNyCBUW6O7KX+5Ary1kNoPjFiF/lP0fy0IRay/PW98S8qVd2Gpryfc1ETWRRdR/cILeBZ+jWvsWHwbNoLNhmPkSKTfr9/X+g0ES0uwZmVhy8wknJpqzssNezzIYJCUE46n9tXXqH7hRernziX9zDOwpqUhnE6wWEwxCe3apb9XJcWEamrQ6upwjBiOY9hQs6z2oUNxjBpFYOdOnOMPAQm1b72FDAQI7tqFY+hQtEYPu+65m4a5c8n95c1Y09PbfNfdkybjWfAVmZGocUtyMu4pU/AuW6aPywPOMaPxb96MVluLa9y45mMnT6bmf//Dv2kTvjVrSDv7LDLOPRdbTg4lv/q12fE16tY9eTKehQtp+OILsNnIv+vOmGDJzrBlZaNVVaGZIpWNa8wYGuZ9RmD7drSaGrNtSz/nHFxjxwLg37gR18SJBIuLEW434QZ9aMuSpnvKhBDk3nwTxdf9TL/fkR3HqAiLBfuQweaQo2vsGDxLFscItd+YGVBTQ9M330IoRPKxx+JZuJDko48290s5bhYNn36Kf9MmgqVlZlR/S1JPOglLUhK1b88hado0NMOizs7GmppK0hHTqP/gA4TbTf5tt8Zdp4nioLaoATIvuxSEoObVV+I7IDLvrmn591S/8AI7L9dfMGtmpm5RN+ym+vnn2fPoY7D9Kz3akOYenmv8+E6DyYI7t2JxObBpZYhwiNybb0Krqqb63svgb4fCkqf0wJotnxH8Tl8LJVxfByE/od3FWHd8TNLK32KxQ8Ocl8BTRXCd7rKu+tdTaLN/Af88Cv42Ff5xBHz7b3j7p8iXLqDizp+y5dTT2X3D+QCkbrob1+vHkD4yQLCsDJtbw+qyoFmy0Jp06zy54QP9Hl++g+DWDdjrl+PM1SO5/Uc8RKBRj4wOr5yjN9KXv6mPJwFak0Z445c0bPaSPikb97BsfP7+eILjcQ3vR9o1vwMEwbJSqNgAx90Ov1xJYLxuQTk938GHt2Dz7yC8bSnsWEjY3R8xehbiyrfo/+yHZJ1bQN5/CuEXS3ULbuqVYE/Cef7tYLXiyz2LYPaxaH4L7lOuBJsDxyGHotU1ERhyLsW33EPJn/S5skk3/Q9O/L3uGTjp/8j85QMEG21ITZD501/BpAtJv/pGQhVVYLeTcekVWKeeAUDohEdpHHkvhAXpF19BynEz8a5cQ9jnw5qRQc2X6wk6MnRRm3g+gbBu4Wq1tTR9951e1zNn6v8/5hhwJGEfOBBLejq+desAqHv3XbTaWjLOOxfH8OGEPR4aFy4Ei4VwYyOexYtxDBtKSuQ86eeeh2vKZHyrV+P9/ntSTz6p+UF0Z5giDSCsVtJOOw1rejruw6YSiMx5dY4Ybu5jzcw0PUb18+ZT99XyZpHugFB1DVitICW7//hHghHBdE+dimPYMJq+1+MdgmVl2Pv1w+JwYE1NxT54ML4NG/Bv344jEvUfTk83E3QYHhv35CnYBvSnbs4chN1uBsVZnE4cQ4bg36wLdbDUsKhLCUTmjjuHD9ddzpGgQ8eQoeY4qmvcITjHjEY2NbHzyh+x9dTT2H7xJWw99VQaPp5L3m23djhtLePii8m7/XZSZs0yt6Wdeir2AQNIOb5Av/7o0QR27ECrrsY+sNmidk+JuIzffY9wYyPuiZNwT5mCfeBAbHl5pkAbLnD3oYcC0Di/EOfo0V0SadAt6nBTE4HIWK41Oxvn6NFotbU0fP6FXtaIODtHj8I+eDDC7dat2Zoawk1NaNXVZjyNNa2585I8cybuKVOwpKRg69ev07I4hg4DKfX9BwzAmpFBuKkJGQggpcS3YQP2QYMAqP9Ab5/63XM3Q55/jvTzz2u+bsTT0fTNtwRLSsxjWmJJSiL19NN0L6HHQ6g64lWITLNNPV6fBpn7ixuxtyP2PclBL9T2/v3xT51K7RtvtkoFF0PVVj2g5cE8eOpYggt1V3S4qQksFpKOPJJwVSk8OhZt5yqCZaWEnz0TXr4Ygj7869ZhTUvDOayf3uuUEnatgreuhYdH6hPt/Y2w9h2Ci2Zjd3oQ/z4enjySpD2zSe7vo2pDFvW7M2Du7+BPQ+ClCwgsfA0Abeda5HNnEPb4sA4eh+X8J0kZDA3zv0Q+PILQe7/Xy+vxUvnKu3DUz+H0h8GVDh/dilz5JqWbjqZybSoiNYemogDufAv2aWfCMTeRe9FxCCskTxiMtd9gtJzD0CZfq9fhnd9DVjLebdVongD2QQNw/OJtsNvxlzfhl/qDG55xB1z+Bow+Gc2rjxWH+s0gcN47yJAk6Yq7cJ1yBYHyOrxbd5N80tnYR+uNUfCs1+BXq+H4O8Cdib+0CmtODta7NsPNK7AMnoyWMgJ+u5UwbizJSXrZho4l/09PYc0bpAvGoMPhjL/ATz7FcvxtOMeMwbduPb6RemPqOkaP8jZcnbWvvQbhMDk330TOzTfhnj4DZt4Ct6yFY39F2plnYk1PxzV5smntpJ9xBsLpJO2UU7BlZ2PN0t21WuYUAvWAzYbzsodIOe9qADIvuZjMH16G56uFWCqbx9kC27fr4gVm0Fq/u+8iafp00iJT34QQuMYfgm/dOmQ4TPV/n8U1fjxJRx+NY7h+D1pVlSkEWnU1jmHDcI0fz6gvPif1pJNwT56iR8BLSepJJ8f13iQdrrtLhcuFrX9/c7su1LpVVfmf/1DxxN/aPUf9p5+y569/Nctlzcwk/dxz8SxabAYl2gcOwD5okOnGDVVUxETPuw45BP/69QR27DSt3nBGa6G2ZWUy/PXXGfnJXMYs/Com2t45elSz6zvSQQjX1+NdsRIAx/DhCKsV+1B9Tqxj2FAzUYd7ymRzVoZ35UoyLr0E6fPhHDWKYW/MJvsnP+mwHu35eWRfc3WMmzXrR1cy8vPPsDgc+j2OGWOOJ0e7vu2DB2PNzKT2bT05jXvypJjzmtm+InVnCHW4oQH3xK7nGLBlZQPNwwS2iFAD1L75Jpa0NAb/80nSfvAD073sGD6MwNZt5rxqpCQYGQK0pjXHngghGPj4Ywx++qm4pvcZ49TOMWMQQmDNyNDvtbaWYGkZ4YYG0s7QO8gN8+ZhSU7GPngwyUcdZdYr6O2/feBAPN98Q3D37g5FNuP885FNTdTPm4dWVY1wu7Ek6e1MxoUX0v+hB8m66qpOy94THNSub0AfZ5qcTnhZPfV/+SkZV/4Mcg/B+/lr7Hrifwy+YjR2/w4oXwOOVJh6BZSvJfjtOyBSGXRcPYEajWDxJ4Q9YRhRQPjDdSBtBAecibPoA/j3CXg/r8TpDmHb9g6hqhTk45MQ9cX6ZPz+k/Xo38/+D5AEmwZjGzMZzr8IvngAvv0X/S88jpL5TkrfWUPoupvJGlULIwoIbn0VWItWV4ssK0WGU7FOuwCmXoH7Yh/1f3oU7eh7CPk3wcpFpBUcSc3CZeQcdbv+cB95HRR/Q/mTL9Hw/efk3X47WVdfhXfFCj3wa/BgAOzAsKM3YcvNpeT6GwjX1enudosFS+4gfCPGwYqVQAjHWb9D9DsE57Bh+NatI1Ckj/dJa7N1Zrrqq2vMcUH7gAFYkiPjfKEQyTOOwR7pXQd37W6e54oeJOIcMUIX36zhWAaMIbxnBdjdhJuazBeoM1wTxtM47zNq0d2OxlQmpyHUb+gNUM7PftbmuJPF5WLIC89jiZrCZk1PZ9js1815vEavO1RdTai8HFtuLsJiIXnGMfS7//9IO/10wo2NVD79L3Lu+z82PvIXhr36Kv4d20k64gializB89VXWHNzcAwdytAXno+9h/HjqXnxf9S+/jqBHTsY+NijCCHMewBIP/ccPF9/jQwGzU6I0egblpl96BCcY+KbLpY0TU+Y4Rg+PEZkrJkZZrBRqHyP7iqtrTUb0miqnv4X/h07yPvVrwjVVGPLzMQ99VBqXnkFzwI9QYe9f39seXmmOIQqKsxxZQDXIeNoiETFm+PIaemENm6M1LneabBmZcUIfDSOUaNo+PwLwn4/wbIyLElJhJua8Cz8CmG3m423Y+gwAjuL9MZ9wACGv/cuzpEjCft8OMcfQubFl5B56SVx1V9nRItVdKciWqiFELgmT8Lz5QJEUhKOqGmNtrx8wnV1hH0+QnvK9biEKCF3dSMZkDVbf479mzZDRBydkU5KsKiI5JkzsQ8cyMDHmrOGOUeOomnp0phgSv9W3RNjSY1NKmQfMCDm/jqiWaj1urFFni+ttpZgZDgw9fgCql98kXBTE+5ph8c8p9EkHXEE9R9+CKEQ9g6GP92HHYZ90CAa5n6CJS01JmmVJSmJjAsuiKvsPcHBbVEHvfDqpYxregl7mqDuiyXwwlnwl1E0vfpH/GUNVH28CpJz9CQGNy2FMx+HH39CMO0w7EkaqSeeTPavf48lJYmwZkVeMQfNro/d+kddDac/QqC8Bn+NlZTjT8Z6+PkQFoSzJlJWdiq1I/4M13wMFz6nBwNd9jrBQDL2URNh8sVwwyI4/RHsVz/HsFdeJumoo6h6ZwHyjL/CxAsIVPsA0EhD+4k+LchoFO1DdPdccOCphCy52HJyyL7pt0h/gNq35+h1IAS135ZQ8+7nZF1zjd67F4KkqVP1gJkoXGPGYMvMxJqRoUd919bq43wWC8HBgyEU0q8bcR85R4/G8+23ZqR1uElPbi+lbI76rq42rRhbv36mpSKcTtxTp5rWWnBXc2S+lBL/tm04osayLMlJZjCfHmUan1C7J0xAq6vDs2gRebfdioj0tu1DhujxBHV1JB99dIfBIa5x43C0cJm5xo41fwdrxBLRqmsI7ik3k3cIi4XMiy/WXbj9+9P/gQdoOu44wo2N1Lz0P7SKSv3aLhcyEIgRqJb3IINBdv/f/ThHjyY1Mvfb1r8/IuLedB96qNmoGo2cgX3QIJyjR5Nx/gVxJytxTZyIcDrNJDMG1kjmr1BlJVqV7h70rlnb6vjg7t26F6CpCa3Rg1ZdgzUry7T6Gj6dh3A4sGZnY8vLJVRZidS0Vha1M2rM1ihLOCMdraoKGQqZswqsme1nAnSNOwTCYXxr1xIsL8d9uN4JafpuKfahQ8zfPuP888j+8Y8RVivCYjEtaYvLxYi3306YSLfEMXQoRNzuLYXMPUnvZLnHj495Rm1GspdIHm1rTjbWtDRzrLxbFnV2s0VtzcxE2GwRj5Fet+5DW8ceOEeOILRrF/4NG81tge26UFvTu5+90HiGjXFw410L19XpMRkWC86xY81nwnXI+HbPlXTENDPItuV7HI0QguSjj6Lp++/RKiuxRuqjN3BwC7XNBa50to76CemX/4ymcifBU/8NJ99PcLDuWqzdIAie8i89UjU1MnYiBMFgGvZDjoQLn4XpP8My42cQlshgkLBHF6TAjh0w/ToaButBWKnX3oN1vD6WEZr5EHVfrWHPX59E8zTBxPPhpN+jDZxJuL6++YV0psL06yA5G+FwkPnDywjt2YNn0SJkOKxHpwqh59WNuBwtkZfR3j9ijZbtMhs417hxuKcdTs2rryI1jfpPPmXXvb8necYM8n5zS1zVZs1IR6urI1xX1+xyihJ1U6jHjIaoKHNjaCHs8ZiiHqqp0QN47HZsOTnYsrKwDxxI0hFHYHE6sTidWHNzYuaSh/ZUEG5owDmyWbisKSmEGxv1lY283vgt6kiDlXzMMWRc0tzQWpxO7JFOQvKM1hG7XcGWmaGXu7qKUPkesxFtScYF59N40YUkH3MMtW/oiVycI0eYY6/OUW1bu8mzZpFx0UUM+POfGPbGbHMan7BYcAwdqotdfj6u8Xpj1TJbmxCCEe+/R87Prov7niwOBwMff5ycn8eOvxopOv1RyX98q1eZf8tgEBkO0xhJyAK6mGjV1VizMrEPGoQ1Oxutrs6cimTLywNNI7hrF+GGhlaubwPjvsLp6SClOe8cdNd3eyQdfhgA9R/PhWCQpCOPMMsa3RFJPfFE8n79q7jqJ5EIu133Hlmtel1EYXhDXJEpTga2PL2OQuXlhMr3YM+LeHcGDNA7WN1ItGN6hnbvNkUbmi1+o5MVjWHlNy5YYA7jGBZ1W9Mu48U99VAyL7+c1JP1oZpo17d/8yYcQ4dicbtxRKZJRT8nLYlOw9rZ+LL7sMMJ19XhXbGyV6WBPriFWgg4/xlKBp+tR1hKSf3aepjxS0I+O9acHCT6cmYtCZaVxfyohstWq601o1EDO3YAUD/3E1wTJ+IYNAhrpNH2rtJXWNHq6qh5+eWo8+qBGu25gFILCrBmZFA3Zw6higqk32++DEYEuOEGMoIyQrt3xVgiWZdfTrC4mJ1XXUXprbfinjKFQX97os152m1hSdeFWqutNXvowYhQW5KTzZfGbAyE0N2J3qZIHdWZ9xiuqyNQVIy9Xz/TNTXoqX/S//7/M69nHzAgJrl/YJseZe+MsaiTkYEA4QY9eC9+1/cE8u+8gwEP/7mVNWmMeabMmBHXudpDOBxYUlPRqmt013c7Qm2QfuYZzT38ESPMTG3O0W1b1NaUFPo/cD/p55zTKkAo4+KLyLrqKoQQpJ1xBikFBQkLdkk94fhWVr5hUfuiLKjo5Sh3XnElRT/+iS6KkfoOVVSg1dRgy8xCCGE2+EbglOGB8EUs82ihtuXn69cUQveCAOFIkFJoTwVadQ3C5erwebDl5OAYPlx3f6JbaUZn1zFseLvH7UvckyfhHDG81TvqnnoYrkmTYoMAwRx2CZbviXnm3JMm6V6aiIXeFaxRwhRtTTrHjgGLxZwPHY05/W3jRn0/MIP0LC3S0XYFi9NJv3vuNvMyWKNc34HikmbXeGSql2t8+0JtHzxY7wAJYXbO28MY8gk3NZlDAb2Bg3+MOoJj2DDcU6ZQ9+FHZF97LcGyXbgO0bPS1Lz0EhkXnI97iu7a0cffyrFFianREBiRlqBntgqUlOBbs4a8W3Wr2rA4jAw29gEDqH72WTIvuRhrRkbMeG1bCIeDtDPPpHb2bDNYwj1xIoGtW8152kYjY83IQLjdpkXtnqSPUaWedBLOcePQKqvIOO888m79TfPYcBxY09N1C76yynywZWoqtn799GtGGmBDqO0DB0I4jIys62q4vR3DhxMsK8O3fn3MC9IyZaq9/wD8UWvpGj3y6PmWlmT9pTeCiCxJ8eXgFhZLbFrMKJKPnQnCkhBhs2VlESguIuzxYO/XsVCnnHgSwulEhkI4Bg3CMaJji7ojsi6/3Pw7+ajpJB81vcvn6AqGUPsjY8TOsWPxrl6lr3IVDuNdt870siQfNwvPlwsI7d6FVlcX40Jt/Pxzc9jDsCJ9a9dEPjcLtRAC1yGHECgpMYOEtIyIUFdUmJZ6ZyRNm2ZON7P3749j0CB8dXWmN2N/k/+73xH2+Vptt6YkM/yN1iuDma7v8vKIO1/3GvS///+6vZa2JRI8FW5qirGos6+9lpRZx7VpITsGDwabDUIhnCNHESwpJVxfjyU5OW7DIB6ihTpYXGxayennnQtCmMM+bSGEIHnGDLwrVphDX+1hHzRIn6NeUWEG1/UGDm6LugVJ06fj37wZGQzqEYD9+5P7y5ux5eWx65579chY9F4q4XCMmBpCZ6yTKlwuAjt2Uveenkwl9dRTgeaGzBDq/g89iNbURPHPb9RXnTGFuoPow4suQmoau+67D2h23wZ36ha18dAKIbD360egtAStutq0RITdzoh35jBy7sf0v///urzSlTVdP7+eOax5ikX2j68h85KLzc/2QYMQLheOkSP0MeTIGLWZKCViKQaLijrsydoHDCC4a5fZwPi3bsGSmhrTYBu9cyPSNV6LuiOyf3wNQ/77n70+D+jWiDG305bXsVBbU5JJO+00XOPGIRwOUk88keTjZnVoFfQWrBkRizoi1KknnohWUam7YHfvhmCQ5Jkzsaank33Nj4HmKGLD25RkWNSR98sQHWMpxJbu3/y77mTgw382P4fTDYt6TyRIrXPLJzrph33AAHP4xoic399YkpNjxLHT/VNSEG43dR98QLiuzqxTIO44hLYwLGlbTnNZ7Hl5pBzbttdJ2O2mdesYPMi0gC17MT7d5nXcboTDQWD7DsIeD47B+u9nz88n52fXtRtIZpB/110MaRGk2eZ1hMAdsap7k0Xdp4TaOXIEhEJ6coGqKuz9+2FNSaHf7+/Fv2kTW0//AdUvvdyme9oQ6tBu3aJ2jR+PVllJzQsvkjxrphmYZVocm/XFDJKPPpqBjzyC9/vvKbvjTkJlZQi7HVtuTrvldI0dQ+7NN+sp8ywWswE3LOroCFt7/3741q4DKWOEbW8w3N3S6425VtaPfhSzepiwWMi75RayrvwRwp1kjlE3W9TDzH1tAzoWahkImMFJwZJSHEOGxDQ4Zv1HLGqRAKFOJNasLDMBha2NJQxb0u+B+xn64guAHqw25F//6vK81/2BIbaB7dv1zE8zjwX0POqBYj3yN/vH1zB6yWKSph+JcDr1ZEA0j4G6Jk8medZMUo7Vj7VlZ+vpONfqc8VbRm87R46MGR8Np6WBEBGLuibGZdseSdN0obamp2NJTjYb+pbBcgcKQgjseXn416/HmpFB6mmnJeS8xm9k7YI1acw5tw8a3OyqTk2wUEei0I14CHuLQNjOsKYkx6zQ1hFJh+lCbctuv43e1/QpoTZcqZ5FiwCw9YukVDzhBAb+/W/Y8vIof/BBGr/QA2EcMWPUujAEy3WL2lgLWaurI/sn1zbvl5qqB1WEw2ZPM+20U8m54QYa5s6lceHXerRuJz3A7J9eS/KsmThHjTJ72oGiIkRSUsw8QVv//ma2pfamp3SVaCva0k7GJYOsH11JyrEzsLjdhA3Xd2RqVvQqWZ1Z1NCc3lGrqmrVm7WkxAp1vFHf+4rowBN7J2PUoAdrdWU4ordgljscxpaXpwfxWCz4128gUKx7fOyD9U6WESjm36B7GozIbIvTyZBnnjGHmoTNhjUnm3BdHdhsbU71isFqNTtGWnV1h4FkBsbUIGM4K/Oyy+j/pz92fq1ejOGJSL/g/L1aSjaatizqzjBmZzgGDzLboL0JJGu3bOnpzcNiXRTqrpAya6Y+NW1s++70fU2fEmojw1Lj118DseKRdvLJDPrnk2C3U/OanmQkOtGDJamFRT1RHw92TZpkRpFCc88PYqNvM6+8AuF04t+wIa65hMJiYfA//8mw1141xVKrro4RUQB7v+YyGr3ZvSU6HWK8DZkeTNZ6jNrA3r/9e7ZHrO1gmd7hCNXUtBofsrawqBPh+k4k0VZdZ8FkBzqG18iWn4/F5cIxbBi+jRsJFpeAzRYzRm+M9wEdjiXbc3Vrx5aT02knFvTOkG/tWkI1NXFbf3m3/kZPr4seV5Fx7rlxHddbseXngxBkRs1m2OtzZhsWdfxu3+Tp0/XFbEaNinJ9d9zB7w7WjIzmxDA9mB3MMXQoY5Ys3q/LD7ckLqEWQpwmhNgohNgihPhdG9+nCyHeF0KsFEKsFUJcE++x+xJLcjK2/v3xLtUTrxvTmwxsmZmknnAC0ufDmpsT00s1x6ijLOrkWTPJ+81vWo0JGe7B6PzBtsxM0iPZpuKd9C8iq1RFjzG3FM7oe0iYRR31ktniFWq321wGNFxXhyUpSX9pI3XTsq6jibaopZSRAKGWFnX3gsn2FYZVZ0lPPyBc2HtDs1Dr4uoaNxb/hg36gi0DB8QEEcVEcHfQ+Bvj0vE+w5lXXKHP0/Z64womA30VqbQWK1wdyGT+8DLy77kbRyQaPhEYnZ6udPqTjzqKMUsWY8vMNIf0esSiNma75OZicfeu97+n6VSohRBW4EngdGA8cJkQouXs8huBdVLKKUAB8KgQwhHnsfsU54gR5tSYtnLOZlyg58BuKaaG69uwqK2ZmQx55pk2o2xtkYCbloknsn50pb59SNfcNsJqNZeMa5n8P9rq7wmLOt6esUhyx4xRWzMyEFareS5bv/Zd39a0NCwpKfo8Wk8T0u9v5c5sLdS9zaLWG7h4x8EOZIyOqDF31zlmLMHSUnzr1uMYFPtsRweGtbdwRfR+8Qp1+nnnmuOyvWm+674kaepUsn74w4Se02hDutuWWA2LOq3nhLqr49MHA/HEzx8JbJFSbgMQQrwGnAOsi9pHAqlCNy1TgGogBEyP49h9imPECDxff401O7vNcZ3kGTOwDxhgzs8zMF2vkelZHc0RNBuyFkLtHD2aIS+8gGvc2C6X25qeTri+vg2LWhdAa0ZGp1MP4sWSmqpbwlJ20/XdnCjFmpWFlBJrSsfjsbb8fELl5e1mmjKDyfb0VqFudgcf7BhTEA1xdUae58COHSS16LgawmtJT+9wbm9XhVoIQf/7fo8lJXmv1mRXxJJ+9llYM9I7nW/cHrYcY4w6scFk0CzUPTk+3VuJR6gHAsVRn0vQBTiafwDvAWVAKnCJlDIshIjn2H2KkUSjvQdRWK0Me/01MzWjud3hALsdGQzqcwQ7SDlpNS3qYa2+S55+ZLfKbU1LI0hrq8TIlZ0otzc0W/DhujpzqlZnWNy6UBvpQ42xdFt2dlwdCHu+vhqQschCq2AyYx67GUzWu1xfhlVn62QO9cGA8XwbnZLopRkdg2PdsMZMBEPc28PcrwvPsTUjgwEPPhj3/orOsaank37WWd0+3gwmS/D0LFAWdWe0NSmv5Yz6U4EVwAnASGCeEOKrOI/VLyLEdcB1APn5+RQWFsZRtPhobGw0z2evrycLqLXZunyNXIcDSzBI0OHo8FhndhbOo6bz1dLvul3mlmSEwziBkro6Nra4dm5yMvVWa0LrLNvhwAYsXrMauWVzTB22RdLuXaRqGl9+9hnZu3cRGjyYbYWF2I8vAE2jqJOypYUljqIiyr5cQCawcvsOQsQek+t0YolY7Qu++SZm3e79jaW2llyg1OtlUwf32lk9Hggk1daQCqzZVUawsBCkJDc5GYvHw6b6OvxR9+co20Um0NjJ8+koLycT2FZTw9pO6udgqMPeQI/UYyBA5siRrAuHCSX43K7y3aQDWxsbO31G9hX77FmUUnb4Dzga+CTq8x3AHS32+RCYGfX5C3SXeafHtvXv8MMPl4lk/vz55t/Bykq5buw4uevBh7p8ns3HnyDXjR0nt555ZgJLFx8lv/61XDd2nKx87rlW35Xdc6+sfLb19r1h20UXy3UTJspwOCyljK3Dtqh64QW5buw4GayulhunHyXL7ruvS9crf/QxuW7CRFnzxhty3dhx0l9c3GqfTcfOlOvGjpPrpx7WpXPvC8KaJsvu/b30rlvX4X6d1eOBQM2bb8l1Y8fJQEmJuW3Hj66S68aOk97162P29W7cKNeNHSeLbryxw3P6i4rkugkTpWf58k6vfzDUYW/gQKvHhq8WynVjx8mmVav2d1FMElmHwFLZjibGY1F/B4wWQgwHSoFLgZYRDEXAicBXQoh8YCywDaiN49h9ijUri+xrf2Ime+8Kxjhpy+Xb9gVm2tA2XNHRebMThTU9PSZdaGcYrumwpwmtjbH0zrDl50WS0WzRP7fhKrUkJ0NFRa9ze4M+na7//923v4uxT0g74wfY+uXHTJFxjRtL0zffmBm/DIzgus6yhzkGD2bsN0sOyLnlin1D8jFHM/R/L5qpkvsSnQq1lDIkhPgF8AlgBZ6VUq4VQlwf+f5p4AHgeSHEanR39+1SykqAto7tmVuJDyEEebfe2q1jDTGypHY/2Xx3sZr5vRM/P7EtuhrwJiLiGSwt1ZNhdDFq1EgS4tuwAeF0tpl5zAjg622BZH0Ni8vVaiGTrKuuwjVpMtYWQZaW9HRsublx5dRWIq3oCGGxxKyE1ZeIK2u6lPIj4KMW256O+rsMOCXeYw9UjIbEmpL4qQedYVjS8QZ37S15v/lNl/Y3MoUFI9mpupp+zxYl1NbsrDYteSXUvRf7gAGkt5EfQAjBiI8+7JVeEIXiQKH3ROMcADS7vve9RW2k9OsoR/j+xBDPQJEe5N/VchoLWYTr6tp1k5r1rxr9AwprampCV1JSKPoa6u3pAqZFvR/GqFNPPZXB6em9dg6hkSnMyPfcVde3LSdbz5Guae2mLzTmYiuLWqFQ9CWURd0Fmseo973r2+JwkDJr1j6/brwYVm7QsKi7KNTCam3OitROSkijoyR6WfpQhUKh6EmUUHeBZot637u+ezum67u4WF/hqxuBQcY4dXuLLFiS1Ri1QqHoeyih7gLmGOl+CCbr7RhR2uH6+m7nCbZHFnlob5EFFUymUCj6Ikqou4BpUfdAwvkDnegAr+4KtRFQ1t4iC83BZEqoFQpF30EJdRfYn2PUvR3hdJpLWtqy4190PhrT9d1e1LcKJlMoFH0QJdRdwD5wIFit5kIYimaEEKaAdncKmeH67iyYTAm1QqHoSyih7gJJ049kdOH8VmtVK3SMaGxrN13fKccfT/YN1+OaMKHN763mGLWK+lYoFH0HNY+6CwghErqc5MGGxZ2Exl4sOp+WRt4vf9n++ZVFrVAo+iDKolYkDNP1ndMznRnHsGE4xx/SrsWtUCgUByPKolYkDCPyu6fSnFrT0hjx9ts9cm6FQqHorSiLWpEwTKHuputboVAoFK1RQq1IGGYwWTenZykUCoWiNUqoFQnDkpSEJT0di8Oxv4uiUCgUBw1qjFqRMDLOvwD3oYfu72IoFArFQYUSakXCSD5qOslHTd/fxVAoFIqDCuX6VigUCoWiF6OEWqFQKBSKXowSaoVCoVAoejFKqBUKhUKh6MUooVYoFAqFohejhFqhUCgUil5MXEIthDhNCLFRCLFFCPG7Nr6/TQixIvJvjRBCE0JkRb7bIYRYHfluaaJvQKFQKBSKg5lO51ELIazAk8DJQAnwnRDiPSnlOmMfKeUjwCOR/c8Cfi2lrI46zfFSysqEllyhUCgUij5APBb1kcAWKeU2KWUAeA04p4P9LwNeTUThFAqFQqHo68Qj1AOB4qjPJZFtrRBCJAGnAW9FbZbAp0KIZUKI67pbUIVCoVAo+iLxpBAVbWyT7ex7FvB1C7f3DCllmRAiD5gnhNggpVzQ6iK6iF8HkJ+fT2FhYRxFi4/GxsaEnq8vouowMah63HtUHSYGVY97z76qw3iEugQYHPV5EFDWzr6X0sLtLaUsi/x/jxBiDrorvZVQSymfAZ4BmDZtmiwoKIijaPFRWFhIIs/XF1F1mBhUPe49qg4Tg6rHvWdf1WE8ru/vgNFCiOFCCAe6GL/XcichRDpwHPBu1LZkIUSq8TdwCrAmEQVXKBQKhaIv0KlFLaUMCSF+AXwCWIFnpZRrhRDXR75/OrLrecCnUkpP1OH5wBwhhHGtV6SUcxN5AwqFQqFQHMzEtcyllPIj4KMW255u8fl54PkW27YBU/aqhAqFQqFQ9GFUZjKFQqFQKHoxSqgVCoVCoejFKKFWKBQKhaIXo4RaoVAoFIpejBJqhUKhUCh6MUqoFQqFQqHoxSihVigUCoWiF6OEWqFQKBSKXowSaoVCoVAoejFKqBUKhUKh6MUooVYoFAqFohejhFqhUCgUil6MEmqFQqFQKHoxSqgVCoVCoejFKKFWKBQKhaIXo4RaoVAoFIpejBJqhUKhUCh6MUqoFQqFQqHoxSihVigUCoWiF6OEWqFQKBSKXowSaoVCoVAoejFKqBUKhUKh6MUooVYoFAqFohcTl1ALIU4TQmwUQmwRQvyuje9vE0KsiPxbI4TQhBBZ8RyrUCgUCoWifToVaiGEFXgSOB0YD1wmhBgfvY+U8hEp5aFSykOBO4AvpZTV8RyrUCgUCoWifeKxqI8Etkgpt0kpA8BrwDkd7H8Z8Go3j1UoFAqFQhFFPEI9ECiO+lwS2dYKIUQScBrwVlePVSgUCoVC0RpbHPuINrbJdvY9C/haSlnd1WOFENcB1wHk5+dTWFgYR9Hio7GxMaHn64uoOkwMqh73HlWHiUHV496zr+owHqEuAQZHfR4ElLWz76U0u727dKyU8hngGYBp06bJgoKCOIoWH4WFhSTyfH0RVYeJQdXj3qPqsGsEg0FKSkrw+Xwx29PT03G5XPupVAcH3alDl8vFoEGDsNvtcR8Tj1B/B4wWQgwHStHF+IctdxJCpAPHAVd09ViFQqFQ9AwlJSWkpqYybNgwhGh2cjY0NJCamrofS3bg09U6lFJSVVVFSUkJw4cPj/u4TseopZQh4BfAJ8B6YLaUcq0Q4nohxPVRu54HfCql9HR2bNylUygUCsVe4fP5yM7OjhFpxf5BCEF2dnYr70ZnxGNRI6X8CPioxbanW3x+Hng+nmMVCoVCse9QIt176M5voTKTKRQKhULRi1FCrVAoFIoeJSUlZX8X4YBGCbVCoVAoFL0YJdQKhUKh2CdIKbntttuYOHEikyZN4vXXXwdg165dzJo1i0MPPZSJEyfy1VdfoWkaV199tbnv448/vp9Lv/+IK5hMoVAoFAc+//f+WtaV1QOgaRpWq3Wvzzl+QBq/P2tCXPu+/fbbrFixgpUrV1JZWckRRxzBrFmzeOWVVzj11FO566670DSNpqYmVqxYQWlpKWvWrAGgtrZ2r8t6oKIsaoVCoVDsExYuXMhll12G1WolPz+f4447ju+++44jjjiC5557jvvuu4/Vq1eTmprKiBEj2LZtGzfddBNz584lLS1tfxd/v6EsaoVCoegjRFu++yPhiZRtZ5+eNWsWCxYs4MMPP+TKK6/ktttu40c/+hErV67kk08+4cknn2T27Nk8++yz+7S8vQVlUSsUCoVinzBr1ixef/11NE2joqKCBQsWcOSRR7Jz507y8vL46U9/yk9+8hOWL19OZWUl4XCYCy64gAceeIDly5fv7+LvN5RFrVAoFIp9wnnnncfixYuZMmUKQggefvhh+vXrxwsvvMAjjzyC3W4nJSWFF198kdLSUq655hrC4TAAf/zjH/dz6fcfSqgVCoVC0aM0NjYCelauRx55hEceeSTm+6uuuoqrrrqq1XF92YqORrm+FQqFQqHoxSihVigUCoWiF6OEWqFQKBSKXowSaoVCoVAoejFKqBUKhUKh6MUooVYoFAqFohejhFqhUCgUil6MEmqFQqFQHBSEQqH9XYQeQQm1QqFQKHqcc889l8MPP5wJEybwzDPPADB37lwOO+wwpkyZwoknngjoyVGuueYaJk2axOTJk3nrrbcASElJMc/15ptvcvXVVwNw9dVXc8stt3D88cdz++238+2333LMMccwdepUjjnmGDZu3Ajoq4Xdeuut5nn//ve/8/nnn3PeeeeZ5503bx7nn3/+vqiOLqEykykUCkVf4ePfwe7VALi1EFgTIAH9JsHpf+p0t2effZasrCy8Xi9HHHEE55xzDj/96U9ZsGABw4cPp7q6GoAHHniA9PR0Vq/Wy1lTU9PpuTdt2sRnn32G1Wqlvr6eBQsWYLPZ+Oyzz7jzzjt56623eOaZZ9i+fTvff/89NpuN6upqMjMzufHGG6moqCA3N5fnnnuOa665Zu/qowdQQq1QKBSKHudvf/sbc+bMAaC4uJhnnnmGWbNmMXz4cACysrIA+Oyzz3jttdfM4zIzMzs990UXXWSurV1XV8dVV13F5s2bEUIQDAbN815//fXYbLaY61155ZW89NJLXHPNNSxevJgXX3wxQXecOJRQKxQKRV8hyvL17sNlLgsLC/nss89YvHgxSUlJFBQUMGXKFNMtHY2UEiFEq+3R23w+X8x3ycnJ5t/33HMPxx9/PHPmzGHHjh0UFBR0eN5rrrmGs846C5fLxUUXXWQKeW9CjVErFAqFokepq6sjMzOTpKQkNmzYwJIlS/D7/Xz55Zds374dwHR9n3LKKfzjH/8wjzVc3/n5+axfv55wOGxa5u1da+DAgQA8//zz5vZTTjmFp59+2gw4M643YMAABgwYwIMPPmiOe/c24hJqIcRpQoiNQogtQojftbNPgRBihRBirRDiy6jtO4QQqyPfLU1UwRUKhUJxYHDaaacRCoWYPHky99xzD0cddRS5ubk888wznH/++UyZMoVLLrkEgLvvvpuamhomTpzIlClTmD9/PgB/+tOfOPPMMznhhBPo379/u9f67W9/yx133MGMGTPQNM3cfu211zJkyBAmT57MlClTeOWVV8zvLr/8cgYPHsz48eN7qAb2DiGl7HgHIazAJuBkoAT4DrhMSrkuap8MYBFwmpSySAiRJ6XcE/luBzBNSlkZb6GmTZsmly5NnKYXFhaa7g9F91B1mBhUPe49qg67xvr16znkkENabW/Yh67v3s4vfvELpk6dyk9+8pMuHdfdOmzrNxFCLJNSTmtr/3gs6iOBLVLKbVLKAPAacE6LfX4IvC2lLAIwRFqhUCgUit7M4YcfzqpVq7jiiiv2d1HaJZ5R84FAcdTnEmB6i33GAHYhRCGQCjwhpTRC5yTwqRBCAv+SUj6zd0VWKBQKhSIxLFu2bH8XoVPiEerWYXK6+LY8z+HAiYAbWCyEWCKl3ATMkFKWCSHygHlCiA1SygWtLiLEdcB1oAcNFBYWduE2OqaxsTGh5+uLqDpMDKoe9x5Vh10jPT2dhoaGVts1TWtzuyJ+uluHPp+vS89wPEJdAgyO+jwIKGtjn0oppQfwCCEWAFOATVLKMtDd4UKIOeiu9FZCHbG0nwF9jDqRY1BqTGvvUXWYGFQ97j2qDrvG+vXr2xxHVWPUe09369DlcjF16tS4949njPo7YLQQYrgQwgFcCrzXYp93gZlCCJsQIgndNb5eCJEshEgFEEIkA6cAa+IunUKhUCgUfZxOLWopZUgI8QvgE8AKPCulXCuEuD7y/dNSyvVCiLnAKiAM/EdKuUYIMQKYE5lkbgNekVLO7ambUSgUCoXiYCOuFCxSyo+Aj1pse7rF50eAR1ps24buAlcoFAqFQtENVGYyhUKhUPQaolfJasmOHTuYOHHiPixN70AJtUKhUCgUvZjel31coVAoFD3Cn7/9MxuqNwD61CJjxam9YVzWOG4/8vZ2v7/99tsZOnQoP//5zwG47777EEKwYMECampqCAaDPPjgg5xzTss8Wh3j8/m44YYbWLp0KTabjccee4zjjz+etWvXcs011xAIBAiHw7z11lsMGDCAiy++mJKSEjRN45577jFTlh4IKKFWKBQKRY9x6aWX8qtf/coU6tmzZzN37lx+/etfk5aWRmVlJUcddRRnn312m6tbtceTTz4JwOrVq9mwYQOnnHIKmzZt4umnn+aXv/wll19+OYFAAE3T+OijjxgwYAAffvghoC/ccSChhFqhUCj6CNGW776aRz116lT27NlDWVkZFRUVZGZm0r9/f37961+zYMECLBYLpaWllJeX069fv7jPu3DhQm666SYAxo0bx9ChQ9m0aRNHH300Dz30ECUlJZx//vmMHj2aSZMmceutt3L77bdz5plnMnPmzJ663R5BjVErFAqFoke58MILefPNN3n99de59NJLefnll6moqGDZsmWsWLGC/Pz8VmtMd0Z7C0r98Ic/5L333sPtdnPqqafyxRdfMGbMGJYtW8akSZO44447uP/++xNxW/sMZVErFAqFoke59NJL+elPf0plZSVffvkls2fPJi8vD7vdzvz589m5c2eXzzlr1ixefvllTjjhBDZt2kRRURFjx45l27ZtjBgxgptvvplt27axatUqxo0bR1ZWFldccQUpKSkx61QfCCihVigUCkWPMmHCBBoaGhg4cCD9+/fn8ssv56yzzmLatGkceuihjBs3rsvn/PnPf87111/PpEmTsNlsPP/88zidTl5//XVeeukl7HY7/fr149577+W7777jtttuw2KxYLfbeeqpp3rgLnsOJdQKhUKh6HFWr15t/p2Tk8PixYvb3K+xsbHdcwwbNow1a/Qs1C6Xq03L+I477uCOO+6I2Xbqqady6qmndqPUvQM1Rq1IGHX+OsoaW67XolAoFIq9QVnUioTx1MqnWFy2mHfPfXd/F0WhUBzArF69miuvvDJmm9Pp5JtvvtlPJdq/KKFWJIxafy21/tr9XQyFQnGAM2nSJFasWLG/i9FrUK5vRcIIhUP4Nf/+LoZCoVAcVCihViQMLawpoVYoFIoEo4RakTBC4RChcAgtrHXreCkluz27E1wqhUKhOLBRQq1IGEEZBCAQDnTr+KXlSznlzVMobSxNZLEUCoXigEYJtSJhhMIhAAJa94R6t2c3EkmNryaRxVIoFAcQHa1H3VdRQq1IGIbLu7vj1D5Nz/UbDAcTViaFQqHoDqFQaH8XwURNz1IkDMOi7rZQhyJCrSmhVih6gt1/+AP+9fp61CFNozoB61E7DxlHvzvvbPf7RK5H3djYyDnnnNPmcS+++CJ/+ctfEEIwefJk/ve//1FeXs7111/Ptm3bAHjqqacYMGAAZ555ppnh7C9/+QuNjY3cd999FBQUcMwxx/D1119z9tlnM2bMGB588EECgQDZ2dm8/PLL5Ofn09jYyE033cS3336L1Wrl97//PbW1taxZs4bHH38cgH//+9+sX7+exx57bK/qF5RQKxLI3rq+TaFWFrVCcdCQyPWoXS4Xc+bMaXXcunXreOihh/j666/JycmhuroagJtvvpnjjjuOOXPmoGkajY2N1NR0PLRWW1vLl19+CUBNTQ1LlixBCMF//vMfHn74YR599FEeeOAB0tPTWbJkCampqdTU1OBwOJg8eTIPP/wwdrud5557jn/9618JqEEl1IoEosm9c317Q15ACbVC0VNEW74H4nrUUkruvPPOVsd98cUXXHjhheTk5ACQlZUFwBdffMGLL74IgNVqJT09vVOhvuSSS8y/S0pKuOSSS9i1axeBQIDhw4cD8Nlnn/Haa6+Z+2VmZgJwwgkn8MEHH3DIIYcQDAaZNGlSF2urbZRQKxKGIbDdtqjVGLVCcVBirEe9e/fuVutR2+12hg0bFtd61O0dJ6Xs1Bo3sNlshMNh83PL6yYnJ5t/33TTTdxyyy2cffbZFBYWct999wG0e71rr72WP/zhD4wbN45rrrkmrvLEgwomUyQMNUatUCja4tJLL+W1117jzTff5MILL6Surq5b61G3d9yJJ57I7NmzqaqqAjBd3yeeeKK5pKWmadTX15Ofn8+ePXuoqqrC7/fzwQcfdHi9gQMHAvDCCy+Y20855RT+8Y9/mJ8NK3369OkUFxfzyiuvcNlll8VbPZ0Sl1ALIU4TQmwUQmwRQvyunX0KhBArhBBrhRBfduVYxcFBwoRaWdQKxUFFW+tRL126lGnTpvHyyy/HvR51e8dNmDCBu+66i+OOO44pU6Zwyy23APDEE08wf/58Jk2axOGHH87atWux2+3ce++9TJ8+nTPPPLPDa993331cdNFFzJw503SrA9x9993U1NQwffp0pkyZwvz5883vLr74YmbMmGG6wxNBp65vIYQVeBI4GSgBvhNCvCelXBe1TwbwT+A0KWWRECIv3mMVBw/GGPXeur67mzBFoVD0XhKxHnVHx1111VVcddVVMdvy8/N5993Wq/ndfPPN3Hzzza22FxYWxnw+55xz2oxGT0lJ4YUXXmhznH/hwoX8+te/bvceukM8FvWRwBYp5TYpZQB4DWhZ8h8Cb0spiwCklHu6cKziIGFvLWozmEy5vhUKxQFGbW0tY8aMwe12c+KJJyb03PEEkw0EiqM+lwDTW+wzBrALIQqBVOAJKeWLcR6rOEhQ07MUCkUiOBDXo87IyGDTpk09cu54hLqtUDrZxnkOB04E3MBiIcSSOI/VLyLEdcB1oLsrWrog9obGxsaEnq8vEk8d+oK60K7esJrM0q6Pz+yp0R0xG7dspLCi42sdqKhnce9Rddg10tPTqa+vbxWlrGkaDQ0N+6lUHTNs2DC++uqrVtt7W3m7U4dSSnw+X5ee4XiEugQYHPV5EFDWxj6VUkoP4BFCLACmxHksAFLKZ4BnAKZNmyYLCgriKX9cFBYWksjz9UXiqsOXgTAMHTGUggmd7NsGT7z7BARg8LDBFEzp+vEHAupZ3HtUHXaN7du3m5m1osV6X82jPpjpah1KKamqqiIjI4OpU6fGfVw8Qv0dMFoIMRwoBS5FH5OO5l3gH0IIG+BAd28/DmyI41hFHGhhjS21WxibNXZ/F6Vd9tb1rcaoFYrEM2jQIEpKSqioqIjZ7vP5cLlc+6lUBwfdqUOXy8WgQYO6dEynQi2lDAkhfgF8AliBZ6WUa4UQ10e+f1pKuV4IMRdYBYSB/0gp1wC0dWyXSqgAoLC4kF8X/pp5F84jPzl/fxenTRI1Pcs4j0Kh2HvsdruZUSuawsLCLll1itbsqzqMKzOZlPIj4KMW255u8fkR4JF4jlV0nbpAHRJJY7CRfHqfUIdlGBkJP1CZyRQKhSJxqMxkBwiGO7i7ItjTRFvB3bGopZQq6luhUCjaQAn1AYIhXr1VxPZWqEPhkJkwpbfeo0KhUOwPlFAfIBhC2FtFLCSbhbo7Vr9X8+7V8QqFQnGwooT6AOFgt6gNtzf03ntUKBSK/YES6gOEvV1CsqeJFurulDFGqNX0LIVCoTBRQn2AcLBb1MYcaui996hQKBT7AyXUBwiGldlbRUwLa+bf3XJ9a8r1fSAT1IJUeiv3dzEUioMSJdQHCKZF3UvdwkHZXK69cX1bhEUJ9QHI7E2zOfuds2M6bAqFIjEooT5AMMSrt2btinF9h7sfTJZiT1FCfQCyp2kPDYGGmCEMhUKRGJRQdwEpJVtrt+6Xa+9tHu2exiify+raq+lZaY60Xus1ULSPMdyhhFqhSDxKqLvAorJFnPvuuZQ0lOzza+/rYLIvi7/klfWvxL2/4fJMsift1fSsVEeqsqgPQIzfrynUtJ9LolAcfCih7gLVvmoA6vx1+/za+1qo3936Li+tfynu/Y2EJ8n25L0ao05zpCmhPgBRFrVC0XMooe4Ce7s61N6wr6O+g1qwS4Jr1E2SLQEWtXJ9H3AYv3lTUFnUCkWiUULdBQyR3C9CvY8TngTDwS51CgyhTrYnd28edWSMOsWhgskOREyhVq5vhSLhKKHuAoZI7o+Arn3t+g6EA92zqO1J+gIbXZym4wv5cFgcOK3Odu8xFA7xt+V/2y9DD4qO8YeU61uh6CmUUHeB3mBR70vXd1fuM9qiBl3ou4Iv5MNlc2G32Nu9x621W/n36n+zqGxRl86t6HmMhDXK9a1QJB4l1F3AsDD3h1Cbq2fto/HbQDhAMBxEShnX/kYwWZItST++i14HnxYRaqu93Xs0xKC3TlHryyjXt0LRcyih7gL7c2GMfe76jtxjvNczXN2GRd3Vzow35MVtc5sWdVsdBMO92lVrXdHzGMGAyvWtUCQeJdRdwBCIvuD67mqEu1GuJHtSl44z8IV8uKwuHBYHEokmW49xK4u696KivhWKnkMJdRcwXLL7xaI2pmftK9d3FwPnoqdndeU4A3OM2moH2u6QGGKgpm/1PpTrW6HoOZRQd4E+FUzWxesZFnB3Xd/mGLVFF+q2hN44p3J99z5UwhOFoudQQt0F+pJQd9XN3yrquxsWtdvqNoW6TYvaGKNWru9eh/HbKNe3oqcori/movcvMjNE9iWUUHeB/en63teLcnTb9d3NMWpvyBtjUbe1Spg5Rq0s6l5FKBwyo/6VRa1YU7mmR4anNtVsYkP1BrbXbU/4uXs7cQm1EOI0IcRGIcQWIcTv2vi+QAhRJ4RYEfl3b9R3O4QQqyPblyay8N3l1Q2vsq1uW5eP60vBZGaEe5yi2HKMuttCbYxRt/Gi9+Yx6oAWoOD1AubtnLe/i7LPif6t1Rh136bKW8UPP/whn+78NOHn7svDK50KtRDCCjwJnA6MBy4TQoxvY9evpJSHRv7d3+K74yPbp+19kfeOgBbgD9/8gfe2vNflY02Lej9YdOY86n0g1GEZ7vK87ehFOaCb86itro5d31rvdX03BBqo8lX1yd6+MTULwBvse42oopnGYCMSSX2gPuHn7sszC+KxqI8Etkgpt0kpA8BrwDk9W6yeo9ZfC+gNa1fZrxb1PlyUI9rt3N0x6u5MzzLmUUMnY9S90PVtdB76Ym9fWdQKg570evXldyweoR4IFEd9Lolsa8nRQoiVQoiPhRATorZL4FMhxDIhxHV7UdaEYOSJbgh2XajNYLLQvhXqsAybFuu+sCajr9HVMWq3zd2l40C/P7/mjxmjPtAs6r7sljNiB2zC1ifvX9GMGdvSA51pM01tH+wM2uLYR7SxrWXaqOXAUClloxDiB8A7wOjIdzOklGVCiDxgnhBig5RyQauL6CJ+HUB+fj6FhYVx3kLnNDY2mufb7NsMwM5dO9u9RlO4CRs2HBZHzPbK6koAyirKElq+zgjKZtGqb6zv8Ws3aM2dmOWrlqNt0WLqsC221WzDgoVl3ywDYPWG1WSWZsZ1PX9YF7mynWVYdul9x2+XfkulqzJmv+1Vulu5rHzf1n88lAZKAdhWvI3CpsJ29+usHg9ESgIlACRZkqhrquvx+zsY63B/0BP1uNW3FYDNWzdTWJXYc2+o2wDA2o1rKdyd2HN3l331LMYj1CXA4KjPg4Cy6B2klPVRf38khPinECJHSlkppSyLbN8jhJiD7kpvJdRSymeAZwCmTZsmCwoKunov7VJYWIhxvtDOEJSDPdVOe9e47IPLmJAzgbuPujtm+zMfPgN+SE1PbffYnsAT9MAr+t92V/vlThTlnnJ4U/97zCFjKBheEFOHbbFs6TIcGxwcP/N4eBWGjhhKwYT4ylntq4bXYfyY8YzOHA2fwMQpEzmy/5Ex+81bOA8aIT0rfZ/WfzysrlgNuyAjN4OCWQXt7tdZPR6IrNizAnZBflo+xfXFPX5/B2Md7g96oh6dZU6YBwOHDKTgsMSee833a6AW+g3pR8HUxJ67u+yrZzEe1/d3wGghxHAhhAO4FIiJxBJC9BNCiMjfR0bOWyWESBZCpEa2JwOnAGsSeQNdxRijbgw0trvPtrptlDaWttq+v+ZRR48Z74uI52i3VVdc3zaLDafV2aXjoDkYqbMxamO/3uj6NtxyfdH1a7wPmc5MfJqvy0ucKg4eurpGQHfO3RffsU4taillSAjxC+ATwAo8K6VcK4S4PvL908CFwA1CiBDgBS6VUkohRD4wJ6LhNuAVKeXcHrqXuDDHqNsJJvMEPTSFmtoU8v21epbx0FuEZZ8Ek0VfI96xpmA4iNVixWaxIRCmcMWDIcDR07MOtMxkfbkRMYXapQ91eENeUhwp+7NIfYKAFsCv+Ul1pO7vopiYwWQ90E715TS18bi+kVJ+BHzUYtvTUX//A/hHG8dtA6bsZRkTSmfBZHua9ujftyHk+2v1LMOKTrIl7RORirba471XTWrYhA0hBC6bq0t15NV0cetsepYh/r1xHrXRiERPVeorGPec4cwAWgt1KBzCKqxEOuyKBPH0yqf5ougL3jn3nf1dFBOjfeqJNrIvB2z2ucxkhuvbG/K2KQYVTRVA20K+vy3qJFtSmyJV6a3s1nSz9uhu1LfNovf7nFZnlwQrxqLuQKh7MqJ0b1EWdbNF3dLiOXPOmby28bWEXvOxZY/x9MqnO9/xIGa3Zzc7G3bGvWb8vmCfuL774Fz9PifUhkUN4Al4Wn1f4dWFui3X936zqKOWkGxrreYbP7+RR5c+mvDrwV4IdTdc3wfyGHVf7u0bv3WmMyLUUQkpfCEfpY2l7KjbkdBrLipdxJzNcxJ6zgMNn+YjFA7pwaa9hJ6cQtmXXd99TqgNixradm8bFnVTqKlVvmkzmCy8fyxqt83d5lrNpY2lpss+EcQEk3Uhhagh1G6bu0tzzeO1qNU86t6J8VtHj1EbGO9YohtXv+anzFNmvq99EeO9iW7T9jcqmKxn6HNCXeevM8WgLff2Hm+z4LXsqSZiUY7dnt1dHmM1x6jtrdd61sIa9f76hPaq92aMGrpuUceMUceR67s3ur778hh1R65vU6gTnPbRuOaqylUJPe+BhFEH0V7C/U1PCrWyqPsQtf5aBqboidU6sqjb+j56elZ3xoW8IS9nv3M2b21+q0vHGVnJjAUvol+C+kA9EpnQh7c707OC4WCz69vW/TFqI8lMRxZ1bw4m84a8vWrMcF8QPT0LYi0eI+dzZ8+nlJL3t74f929rXHNlxcoul/dgwXhvavw1+7kkzfTkO2q+Y2qM+uBGSkldoI6BqR0ItbdZqBuDzePUWljTrUaLLSalZ1coqi/CG/Ky27O7S8cZD72RRztaxAy3V0It6m5Mz4pxfVvdXQq4ixmjth7Yub41qe2zFc72F9/s+oZjXzvWtOR8mg+HxWF6fKKtZ1OoO7Go11ev586Fd/JV6VdxlcG0qCv6rkVteK36mutbWdQHOca48+AUPdFaexZ1liur1ffGg5dq1+csdsf9XdRQBMR2AOIhOpgMYhOg9IhQd8f1HdawWqxAxKKONCIlDSUU1Rd1eKyxr8vmMt3nHU3P6s1j1HDwj6Ftq9tGnb+OkkY9dag/5Mdpc5p53tt0fXfSuBrPcbyrLhmdtrWVa9tcu7wvYHRwe5XruwenZ/XlpEJ9SqiNxmBQ6iCgtVBLKanwVjAifQQQG/ltPIDdXR0KYGf9TqDroho9PQtihbTWVwsk9uE1XjK3zR1/1LcMxYxRGw3pH775A/d8fU+Hx3pDXgQCh8WB1WLFKqytXGdaWLdUBaLNyPf9TXQ9HewNiXF/Vd4qQH8XXFaX+Xy26fruxKI23sWOMgYaaFIjJEOMTB+JT/OxuWZz12/iIMAQrhpf4lzfO+p28MDiB7qdXa4nE55EB5OFZTjh5+/N9EmhNsaoW1q2jcFGvCGvKdTRwWaGcBhZgDoSsE93fMptX97WarthWcbTGEUTHfUNsa7f6HnhiUrdaFwv2Z7cpelZRpCey+oyG5E6fx3lTeUdHusL+XDZXGZCDLvF3upFN+45xZ4SU8beQnTwXF8RaiOew6f5cFgd+m+IiBHleC1q452Ix9tkDDtNyp0E6BZ+X6Qnor4Xli5k9qbZMUOAXWFfBJNB3wva7FNCbbiIMl2ZpNhTWlnUxsM5IiMi1G24vuOxqL/d/S2f7PiklUsuXtd3SUMJp791ur7YAa2jvqNfguhAkkSN3RgvW4o9Je6paKFwyHR9u2wu80VqCjV12uM31qI2aEuoDQvdyHjV29zffcqijgTzVHr11c38Id2itggLLpurTdd3Z14k452IpxNrrCZnDFH1tUbboCeivo1nt7t1aiYl6oH3M6AFEJHFHPvaOHWfFOoMZwapjtTWQh2xEIanDwdiGw1DLA2h6EiovSEvEtnqBTIs6s4arc01mylpLOHOhXfSFGzq2PUd1ZtO1Dh1dKck3ujNlglPoqOgm0JNHdaXT/PhsrrMz3arvdV1DYvV9Gj0soCyvjRGbfwWhlD7NB9Om74YS5ItqU3Xd2fuStP1HYdFbQh1ujM9pjyd8d/V/+W/q/8b1769HWMNd0hs1Lcp1F2YXhlNT+f6Nn7zg/0da0mfEmpD1NKd6aQ4WlvURtKQAckDcFldMY1GV4LJDNdfja8GT9DDv1b+i2pftWmxd5busy6gC3xxQzGPL3vctMzbsqiNMWpIoEUdNR7fnTHqGIs6qi7awxvyxljUNouttUUdaQDiGXrYH/Qlt5w5Ru3Tx6gDWsDsaKU6UmM6qNHPekf10i2hdqR3et5oPtnxCZ/t/CyufXs70c9bb7Soe2J6VkALxOST70lWVazqVXEwfUqojQc63ZFOqj01Zgy6MdDIR9s/QiDIS8prJeQtg8m8IS/3LbqPjdUbW13HEMwafw2LyhbxjxX/4PFljwO6u64zy9co57EDj+Wzos9aWdRtjVFD4pJKBLUgVmHFaXV2a3qWy+oiJEOEwqFWjXpbGGPUBm26vlsIdW+bSx0tVgd7b9+4vxiLOrK86cDUgRQ3FJv7Rkdxd9SR7I7r23gW4g3srPZVd3nGRW8lWkgTOUbdWy3qUDhESIZMoU50Ap1o1lWt4/KPLmdp+dIeu0ZX6XNCnWxPxm61k+pINRsFX8jHZR9exuKyxfxm2m9Isie1GsNuGUxW3FDMW5vf4t2t77a6TrQVabjT39nyDgCHZB9CQ7Chw95anb8Om7AxKmMUDYGGVtOzWrq+DUu2ZQcgoAX4ePvHXe4ZBsNBHFaHLtTdmJ5liK435I0rMtWnxQq1w+pod4za9Gj0Mte3L+TrM245Q3CNZ9uYngUwLG0YO+ubF4qo9zcLdUcd1O5Y1C6bK+4FYKSU1Phq4p7+1duJ7rhGe9X2FuO37UoKYL/mZ2HpQiBqelaC30+jHdoXFnVZYxmQ2Gj6vaVPCXWlt9LMnpTqSDVf2vXV69lRv4P7Z9zPVROuAiDNkRbTaLS0qEsbSwFYU7mm1XWMh73WX2taHQbjs8YTCoc6fJBr/bWkOdNIc6Th1/xmOdpLeNIvuR/QuiGcs3kOv13w2zbL2BEBLYDdYsdutXcr6tuwrqIbkA6Fui2LurMx6l7m+g5ogW4LtS/kY1HZop4oVo9gBJNV+aqQUprTswCGpA7BG/Kaz31DoKF5fnUHVlBXpmeZQm116cMscVh/TaEmAuFAl2dc9FaMZ6xfcj98mi9hwy3GeY20vvHwyY5PuOGzGyhrLOsx17cp1K4MoGeDyaKnHfYW+pRQb6/bzrD0YYAe0WwIoOG+PrLfkea+KY6U2GCycKxFbfS61letbxXdbTRI1b5qKr2VZLuyGZA8gFx3LnlJeUDHDVKdv450ZzppjjT9PN5qoO0UorW+WjPTWsuHd2GZ3svd2bCz3Wu1RTAcxG6x47A4uuX6NoS62l9tfl/ta/7bG/K2ipJ2WzuJ+m45Rh1nuYJakHu/vjfGHdsT+MP+bvf25+6Yy8/m/YxyT8fT2DojLMMJXe60PczGPBIoGO36Hpo2FGjOGdAQbCA/KR+I0/XdBYvaaXPGbVEbz18gHOh1nbzuYLwPRt0myv3dnTFqw7NS56+LcX0ncozXOG/LdyyoBdscftwbjGG63uQZ6zNCrYU1dtTvMOdIG65vKSUbqjeQ5kgzH3rQhbytedSGVWsItU/zsbV2a8y1oi3qCm8F+cn5PDDjAW474jbz+I4apDp/nRmZDnojIxBmY2iIWFiG9ZSokXnh0RZLUAvy7a5vASiu75pIBbRAl13f0cFkhgUVbUVH/339vOt5+LuHzc9tWdQthbi7wWRFDUXM2TKHL4q+iGv/7tJVi/r1Da/zp2//BDSLyN42tu9ueZdT3zq1x4Uo2oKt9Fbqru/IszkkbQig17vRcWjP4xNNd1zfTqueDS0eUYl+/vZFZ6anMe7ZqNvoDIV74943nt2uuL6jr208exLZrTTL7dHS9W20dR/v+JhLPrjEtIITgRl70YuCQvuMUJd5yvBrfnPqVaojFU1qeENeNtVsYlzWODPhhvF9W8FkhlAYrm+A1ZWrY65lPOzVvmqqvFXkuHM4sv+RnD78dDNhR4dCHajTA96ihNpmseGwRhasiHQaGgINhGWYAckDgNiGcEXFCrPDYMzfjpdAWHd9O6yOLrm+zRSikUY7RqijppAUNRSxvW67+bnlGHVb07PMMeouBpMZv2GPW9SaX49/sNjjEur3t73PvB3zYsq4twJS3FBMQ6Chx8dhvSGv2amt9FbGTM/qn9wfm8XGzvqdNAWbCMtwlyxqT9DTadapaKGOd6W2g06oI/fcLylWqB9Y8gC/KfxNt8/bnWCytoQaEuv+bs+irmiqQJNah8GqXcUQ/e4G1PUEfUaoDWGItqhBd3NsrtnMmMwxMftHB5tB64Qne5r2kO5MJ92ZHjMGrIU18yGq9ekWda471/zemIftCbRvXdT6a0l3xgq1IZzRZTFekP4p/RGIGKFeWLoQm7AxIXtCl4U6FA7hsDpajVE3BBraddnHuL4jjXa0uzv673p/fcxnX6jFPGqLvdVwQndd34ZolTSUtPpueflyZm+cHdd5OiOgBXBanbhsrk6FOizDbKndYpatK9ZkRxjnS2Te97bwhrwMTtXz5Vd4K2LGqG0WG4NSBlFUX2Tel2H1tbfqkZSSxkCj2cHrrPzRQh09FbAjop+3xmAj7299n9PeOi1h2fz2Ne1Z1Ls9u01vX3cwfqOuWJNGLIon6IkZ101k5LeReMm0qCOdPuNZSeQUNUP0lUW9H2gp1JNy9PSD/139X3yaj3FZ42L2T7Gn4NN85sNmJjyJWMQSSa47l4nZE2OEOsYt6Kuk2ldNtjs75rzQ9lrYBuYYtTMyRu2rxm61m8FaRpkMKyHTmUmSPSmmgVtUtogpeVM4JPuQbrm+7Ra7OT3LGGu6bcFt3Lvo3jaPaTk9K7p8bpvb/NsX8hEIB1oJdcvMZC2FuLvBZB1Z1K9vfJ2Hv3s4IYs6+EJ6Gs14XLFljWV4gh58mo+AFujUoq5oquCKj67oNKd1tFXak3iDzUJd7iknLMOmyII+Tr2jfofZcTDEpD2L2hvyokmN/sn9gS4KtdUVV9BP9LBCQ6CBdVXrKG0sPWCjwE2L2hDqiFg2BZsS4vruijVpeMsag40EtID5LidUqCMeNbfdjcPiMMvZE0IdPe2wt9BnhHpb3TayXFlm1ODYrLEcnn+4uTb02KyxMfsblq9hQba0qAGy3dlMzJnIltot5oMTPU68s24nYRmOtagjQt1eYxTQAnhDXjKcGWYwWa2/Vo/CNoQ60mmIzrSWbEuOKcPG6o0c2e9IhqQOocbftWkpwXAQu9Xeam3oLTVbYlz+0WhSi0l4As0v8IDkAaZQG+Wo9deaC22EZCjG9Z3mSGv14hnC3FWL2hC/ssayVoLcEGjAr/nZUbcjrnN1hGFRt8zM1RbRglsfqO9UqNdXr2dlxUoeWPJAXNm9elKotbBGIBygX3I/bMJmeiqif78haUMobig2f8O2xqhXVawyBbal5d2Za7qlRR3PUENL13ei4gL2F0ZnsGUwWVOoiYZAx9M/O6KtYLLi+mLe3/p+u8cYv3NTUI+sN/Px74XruyHQwDVzrzGNDONZcVqdJNmTTMvf6Jwm8nc0ng1lUe8HttVuM8enDS4/5HIAbMJmWtoGhiAYQm0IRfRSjDnuHA7JPgRNamyp2QI0Ww157jxTTHLcOeZ5jQ5Ae42RmZQlyvUNxAi1cV5DCDNcGTEW9aaaTUgk47LGMSRVD+7pyhhtQAvgsDhMV3tAC6BJjQpvRZvlllKaa3VDa4t6YOrAZqGOzKs1AuGMlyHa9Z3tzqbKWxXT2Bj7GfOo420EjBc5JEPs8uyK+c7oNGyo2RDXudrDSMZgWNSdCcemmk0xZejM9W3U3fd7vu+wwezKFKfuYlgZSbYkstxZ5nMVY1GnDsWv+dlSq78TWa4sbBab2Ymt8lZx5cdXmvdi3HfcFnW4Oeo7Xou62leNVVjN6xl1Gt0hrPRWJjyCuKeIzn2fZEuKEUtNat2aviSlbNOifmPzG9y58M52vVhGO1QXqCMsw835+PdiLvW2um0sLV/K9xXf6+eKXNsIIOwp13dTsGmvs7P1BAe9UD/x2WZWVwTZVrcNR7g/j8/bRGmt/kMcP/h4+iX1Y2TGSFOUDIxeYX1Qb8yNxsFhbRawbFc2YzN1S9xo7I3GyJgyBbFCbVjk7TVGRs8wzZlmBsuALtSGEBplibaoWwo16F6CwWm6i7Ir7m8jmCy6Y1Cn1bU5/Wd73XZzdayWY9RGYzggeQANwQYCWiDGsq/x1TQLdZRFlu3K1ue8RgmXX/NjFdbmFcTidH1HX69lZ8X4bm8b5+hGJB4LL0ao/fWdZuUy6nFM5hieWvlUu+c1LepQz1nUxr25bW5y3Dl8XfY1gOn9ARiark/R+mbXN4De6U2yJZmN656mPYRl2EzZa5TbEOrOLOqQDGEVVuwWe9xj1DX+mpjzt2VRP73yaX7xxS86Pde+JBgO8uCSB1u9v+Ya7lYXKfYUs26N/3cnYM6v+ZHonePoqG+jc93W9EEtrJnfG89pIizqlgJsdMYcVkeM18rcL5AYoY6OHu9Nrm9bPDsJIU4DngCswH+klH9q8X0B8C5ghPK+LaW8P55jexJvQOPjNbvYVFlF8uh65q+GT2s28/cvNuO2W/GHwoTtFzM6P5nSWi8DM5rHSd1W/WGr8+kPgNEYF1fpU1GaQk3kuHMYmDKQFHuK2dgbL8rAlIF8v0fvDUYLtcPqwGFxtGs9RYsv6I2c3+vHbrEjhIhJBlLeVG6+qMn2ZPOh3Vi9kVR7KgOSB5gPdDwBZZXeSnMhDofTYXYSAlqA2lAtgOlWMyLkr5t3HZNzJgO0tqgjPW1j/e+WmaGqfdWmez16jNoY06/yVpleBWOurmnld8H1bRVWNKm1CigzGpj11evjOld7tOztd2bRbq7dTK47lwpvBfWB+uagsnbiFmr8Ndgtds4ccSaPLXuMGl8NmS49cU8wHASpR8qbwWQdBCruLYbL0W1389NJP+X7Pd9zWN5hHDf4OHOfw/MOJ8+dxxfF+pS4NEcaSfakZos6EqxjiGRL13fLTuzmms00BBo4LP8wAAIyYD4H0UuqdkSNr4aBqQMpbSylIdBgikq0UNf4ahKa5SsRbK3dyusbX2d4+nAuT7vc3G50Tpw2J8mOZHOqqVHHdf46sz7jJbqDGV2nRlu1y7PL7Pgb1AfqTXE3Oj+GMbI3FrVxH8bvE+367kmLutLXnKDqgLKohRBW4EngdGA8cJkQYnwbu34lpTw08u/+Lh7bI7gdVt664RhG5+mVP23AOD675ThuPnE0lx45hOtmjeCGY2ZSVp7N2X9fyHsryyipaeKOt1dz3X9LkGEbDxW+TlWjn8836JGUZzyxBH9Qr7YMZxZ/+XQjnsY83lrzLY/N20RjpJH8cm3zeGi0UEPrZCqgNxLf7f4uJh85NLvgDRGMTgay27Obfsn9EELEjFFvrNnI6MzRCCFIsieR585je912ShpK2o1ylVJy2YeX8dTKp5oTnkS5vqs1/SU0prSBHsCy27PbtBAN16JhHRsv7oAUffpYy7HyKl+VmQGppUVtfG8Q0AK4bK5mK78LwWSDUgfhsDhiLGopZYxFvTfJGYxGrWUj0haBcICd9TtN0WkINHTqsq7x1ZDpzDS9N9Fj3Pctuo9bCm+JOb6tTmCVt6rLmZZeWf8Ky8qXxWwz7s1tc3PS0JO47YjbOHHoiebzCXqn4crxV5qfU+wpJNuSzWMNq8VohI3ymmPULTosf/7uz9z99d3m55AMmZ1Bpy3+hCfZrmwzNbCRjCe6gTcC/NoKLpy9cXarLIP7AqNz2XKesE/zYRM27BY7KfYUPCGPuWofdM+ijhHqqDo1nqvdTbtbHRM97bKVRb0XwWQtBTi6Mxw9Rp1ooTbqOdme3Kss6nhc30cCW6SU26SUAeA14Jw4z783xyaEZKeNaUP1scl/XHAWo/JS+NVJY7jnzPH89rRx3HbqOObcOIPcVCc3v/o9x/55Pm8tK+GUcSMZ5pzFzsACjvrzeywrqgQE500dTGPk93v0o108OX8rec7hhG1l/O3zjdz7/nIAPB7dFWjFzebdfp7/ejuPzdvEN9uqSLYlt2pMH136KD/99KeUNOovZrRFDZgCFT1lyhBq0C0cYw7qpppNMcFxg9MG88G2Dzj97dOZvUmfjqSFtRhxqvJVsduzm6L6IjOYzG5tFkXDooZml7ExBmkIYMvMZMaqWMa6wdW+6pjczzGu7xZj1NDCDRXqvkWd7khnUOqgGKH2a36C4SD9kvtR66813ffdoaVFHR3Ud9fCu8wAvLsX3s0jux8hLMMcnn84oNeBsX9HFnWGK4MxWfoUwmjX+bbabWyp3UIoHGplZURzyQeX8PTKp+O+Jyklf13+V97e/HbM9mjXd0dcOOZCUuwppNpTsVqsbVrURuPa0vUd7RGQUrKxeiNljWXNMzBkMMai9mv+TjtaNb4aslxZpDhS2OXZZYpxW8vEtuxo7WnawwNLHuCtTW91eI2ewBTqFvOEfaHmuevJ9mQ8AU+b64B3hfYsauO53NW4q9UxMWmCI6JtpjruAdd3y3fMaEcTLdQDUwYeWBY1MBCIHiApiWxrydFCiJVCiI+FEBO6eGyPUhQoYmDKQLKTstr8flReCh/dPJO/XzaVG48fyfzbCnj04ik88YObEZYQo0et5sJp/XBZnTx28RRyU/QeY25SDn+7bCo3HD2TsPBz9XHplNbVAvDkJScBEAqkctY/FnLf++v4xxebueSZJeysDPP5xiJ+8MRX3Pzq93y+oZh5O+ehSY2vSr8CYHVxkEAobI797aoLUvDIfIS0tbKoAdP1XdxQjDfkjZlu9rPJP+PqCVczMGUg84vm69vm/Yz7l9xv7mNYaVXeKjOYzGnRGwJ/2G9a1NDcCBgZ2TSpW+lGZ8JmscVkKTPctNGub4Gg2lfd9hh1RKijLRi/pg852Cw2LMLSpYQnqY5UBqcObnNVJyNt7Ibq7geURY+fRY9RLyhdwHtb32PO5jns9uzm3a3vYsHCsQOP5aQh+vMRHUXfnkVd66sl05VJjjuHLFcWG2uax9Rr/DVU+apijm0p1N6Ql/KmclZXxCbm6Yg6fx3ekDemY2WcC2I7Vm2R4kjhxkNvZOagmQAxY9RGY2hYYMbzlJeUh0VYYjos5U3l+gwBqbG7UbfoAjJgPi8umwtNah1OsfNrfppCTWS6MklxpJjrwkeXAZob/ZY5yY06MDqm+xLjmW3LojZ+g2S73vGPnqfenSla0UIdPUYdbVE3BZt4asVT5jNvdHRSHammBy0RwWTGs9KWRZ1sT241wyFRUd+VvkoEImbYEPS26IHFD+w38Y5njFq0sa1l93U5MFRK2SiE+AHwDjA6zmP1iwhxHXAdQH5+PoWFhXEULT52+HYw3DW803OmAkc4YfOK3RjOxUNch1Ae/JL6qsmIsODLL78kGUkV8OMRTlJrNlG0W39YMsLLOXe05BMP7F6vN8DDk9OYcoiDcVlWMl2CDdUa7zS58WtN2EMePl9Xz0fbP8Q9UH8wvyn7DimtXPfCKkZnrMXSzwMCyuuCWOqbsHg01m4v4vnKT6loquTjZQ1MqP2C6kA1Df4Gnpz7JgCN2xopLG2+38M5nJ1iJ1/t+oqXPn2Jb3Z/Q0lVCYX+QpqCkk9qdQEvqSnBH/ZTWV7JhgZdvL757puYsZsF3yyg1FXKl9VfxtTf1k1bKdylX9OGjRAhREiwYZl+niVrllATqsEl9Mj5NVvXIMv0x2HdynV4Nuj1GJZhBILlG5czoFx3m5fuKSUYClJYWIgVK1t3bqWwvhCP5uG92vc4P/N8s2MRTXltOXaHnTRrGjsadzB//nyEEOwK6NZBSk0KAsEHSz+Ara0Oj4siv97wb1q3iSpfFR6/h8LCQt6q1C2wjzZ8REOJ3rBclHQRo2yjWPvtWhzCwaodq2LK2tYzWlpTymDHYAoLC8kll2VFyygM6vtVearwSR8fLfjI3H9L8RYKvc3nqQzqv926Pevifq9KArolV1RRFHPM6iZd7NetXEfjho7H4gdF/issLKSpronqUDWFhYWsq1yn32+dfr9ra9ZiwcI3C7/BKZxs3L6Rwjr9mmuamnMUfPD1BxziPgRf0EcwqD8LJfV6Oed9OY8kS1Kb5agJ6WJcsbMCrUkzfy+ALaVbzPuratDFcP7X8+nv6G/us9WnPxgrS1cmtF2Kh1Xl+vOxfc/2mGvvrNyJDEoKCwtpqGqgylfFl0ua38fv131Pekl6h+dubGyMOedmn97quYWbyrrK5npp1OtlbdFa/ln9T16ofAGxSzDOPY5vGvSAwQwyKA7onYrKMv15W75iOYFN3RPrdTX6M1JSVUJhYSEbavU2ZPHCxXhqPFR5qvhi/hdmp6qioaLD32apZyllgTLOzjy7w+uurlpNsiWZxppGav215jm/bfyW2VWzyavNY6y72VvZsg57iniEugSIjiAYBMSkvpFS1kf9/ZEQ4p9CiJx4jo067hngGYBp06bJgoKCeMrfKdW+amp31nLCISdQMLHr51y7Yi3/Wvkvsvtl4y52U1BQwHMfP0dpRSk/OP4HWC1WfCEfj77yKLYBNsZaB/HJ93DWCWfx0CsPccig0Tw462TzfGcAO794j9LGUt669jR8QY1TX32JSn8mblsKfksxSdYM7jhvEg9+sJ5wrRNHJhw+JJcHLi7gnHceZXVVgKUbykkZJfEHsvjrSsnEifkEZIB3infgyBbU2A7n0FEDmb+hAqfdQlmtl1VlEwilzue5yncAKA9VU5U6kkc+2URNUimOTKgPN+KwOgi7cmhw61O7bPlD2F1eT6Yzixp/NSMnjKRgcAEvfvIimYFM0+U1/pDxFIzS6zj59WR8Ph9ZqVmcfvzp/N8r/0dKvxQ0v0ZWOAu3zY07w83oYaPhS5hx5AxGZY4y6ynz9UxS8lIoOEY/36vzXkUEBQUFBbhedZE/IJ+C6QW8t/U9Fi1cxI+O+hEzBs5o9fvd9/p9jBw0kiGpQyhcVsgRM44gxZHC8vLlsAuOO+w4tq7dyur61Tx83MNYRNcnQiwvXw5zYdqh06AcClcVMuu4Wdz/xv1YhZXiQDEbbBvIT8pnZPpIjGc7440M/E4/ePUpTGER5uiZR3PXwru48dAbzemEd716F2OHjKVgegFLv1vKaxtf49hZxyKlxPeS3sPPHZsLEc9kWnYa0e/PyoqVUAYN4QamHDXF9HB0RGFxIewC4RIx52ra1gQVcOxRx7aa0tgRc7+aS82eGgoKCnj505fBAz7ho6CggIVLFpLmT+P4448n480M0vPSKThWv+amVZtAX/OBrOFZFIwr4J+v/5PslGwKCgrYs3EPc5bM4YijjiA3KbfNa6+rWgelcNTko9i1ZRfbSrYBupVvT7Wb9xd6NQQajJ86nim5U8zjRbGAcqgIVTBj5gxzSGhf8Mjbj4APgvYgBQUFBLQAFmHhvQXvkVmbSUFBAYu/Wcz6besZP2W8+QzkDcmj4NCCDs9dWFgY89taSixQDrkpej0a3wVejqyI5QpCHlAJIw4ZQcGwArau3grVMK7/OIp36kI9cdREPln2CeMmjKNgaMdlaI9F3yyCetDsGgUFBaxavgrrGisnHn8im1du5ssVX3L4MYcjiyQ2YcMrvRx33HExaaCjefnTl9nQsIHHCh7r8LpzvphDP1s/huYOZWfJTrMOSteXQhWkDU+jYFzzPbWsw54inlbpO2C0EGK4EMIBXAq8F72DEKKfiNSQEOLIyHmr4jm2pzGyhk3ImdDJnm2Tl5SHRFLWWGa6dh1WB1murJj1l4elDWNzzWaaQk3YhA2HxcHJQ09mxoDW4pFiTzFdNp5QLbWsIyV0JA21enT0gNRsLp8+lA9vPpazJo4EINPtZnhOMkOzUpk6NJUbT9QD1O469Wj8IY0vN+guovx+m0m3DOfxeTs4+x9f8/hnm/jTxxt4Y2kJ/V3jQdrZE9RdpyHp5TdvLybJaWVgXsTFFPbREGjky43VPF2oWx7/98EqqkI11NbpLukbXvma3721is01Wzh24LGmuL27YjfXvvAdCzZVmONndR7BPwu30j+5P7s8u6gP1JPmTCPLnUWVt6pN1zdE5lL7YseoDVefw9Kcg3xbrd7olnnaTpvYEGggxZFClrt5nNzYDvo0uAvHXEiZp4zFZYvbPEdntBw/k0jWVK6hwlvBpeMuBWD5nuUcO/DYmIYkzZFmur77JfejIdjAltotfLLjE3PZy2A4SH2g3lyedUzWGPyan6L6opgpKcZqVdA6mCx6CCFe960x57ylC9VwBxorucVLjOs78rs2hZoIakEag41mAFLLQMuN1RsZmDIwJhgwKINmHITx3HTkkjQz+EVc3wbD04ebLlMpZfM72WLowKiDkAyZ9RyWYeZsntPh0p17SygcMtOBVvuqkVJyzSfX8OjSR1uNUTcFm2LK3Z0xauP3yXBlmPUZDAfN33yXZxfrqnVLNzrRiNPqNONQAJLsrVf56yotp10ZCwUB5rtgxPPkJ+cTCAfwaT5qfbVtxitsrd1KfaC+0zzyVb4qst3Zrab9GS54o73Z13Qq1FLKEPAL4BNgPTBbSrlWCHG9EOL6yG4XAmuEECuBvwGXSp02j+2JG2mPNZVrEAgmZHdPqI3MP6WNpaZQ57hzzFWCDAalDmKXZxdNwSbcdjdCCB6e9TDnjGodO5fiSDFfpNWVqwnLMPefcgFXHVYAYK7CNCI3hQn99OsbvXi33Ul2ipVxQ/Rx4SMHj+Sr20/gd6fqFkBtqJSrppzJr04azf3nTGDVfaew7v5TWfn7U3j12pkcO2g6gNmBeOCC/nzwi2PxUkpypLEUIswFhw3jyR8eAcC1x+VjsTWR79ItvAmDHMz+fj21/hp2VWTjFrqAf7WpmhXFdfzo2W+prNdflrLqMI98shGXyGZLdTFrd+8m2ZaKFkxiRVkJ35foc2l//NxKHvpwnfmSZbuyqfZWU+cNsnBzJRWeRqTUHUAOq8NsBLbV6S9OW4Eufs1PIBwgzZEWE9AGzY1vmiONEwafQKYz08xS15I5m+fwVclXbX5nXAd0oTYi1h9Y8oB+XxN/bGamO3bgsTHHRa95boyJGcFDhria0/UiGfWMnPSbajbFjK8aKXJz3DmthCZ6fLPlSm/tsdujjwe3J9SdBZO1JHrua5W3ypwhUOuvNeMIILYTC/oMhnFZ4xiUOshsmKOF2vh/RxG6xm+e6co0OwSgC7VRvz7NZzbircaoo+rA6Ois2LOCexfdy4NLHuxSPbTHy+tf5p6v74nZVt5UTkiGGJY2DL/mpz5Qz7qqdayvXt9qjFqTWkwEdrfGqCNj3FnOLHM2hhHY1y+5H56gh1UVuiveaL9q/bVkODNi6tX4u+XMjFA4FPcYr/EbeIIeglrQjFGB5nehtEHv5BqrB26s3sjxbxxvxvkY1PnrqPBWxLUM7G7PbvLcebisrpg1uY36NNqbfU1cfj4p5UdSyjFSypFSyoci256WUj4d+fsfUsoJUsopUsqjpJSLOjp2X7Kmcg397P3MXl5XMdaPLvOUmT26O6ffyeMFj8fsZ1iMTaGmTq0NozGSUrKuah0CwcwhU/jxtBOAZqGGqOlZInZ6ltGQ9kvuR4rTRv/UDPOYk4aeyK9OGsOPjh5GmstOksOGw6b/1KcMPYVkezJXT7wagNxMD/WhShqDjRyRP808x+CMVMbk6T3XjHT9If35MbrQnDwxjYcu1sXnq7VW6hv0a9/1g4l8/bvjefDciQRDekM8JDOD4TnJrC+xsqO2jHJPDWuKA3y3NYgmGpm9TH/wN5f7+fdX23n0001IKUm1Z7GpsowjHvqMK/77DTvrdrF4U4C731mNBRsBLcCa0jpW79HH1YrqyvCHND5evYsl26qo8wZZVqRbI6n2VHZX6/VX5Y0Vahl24bA6OGfUOcwvmt9qHm1Yhnnku0f416p/tft7RgeT/WDEDzh/9PlsqN5Af/cI8pLymDVoFjaLjen9p8ccF50kpH+KPiZqCIEh1NHWIOi56q3CyubazTEBNIal1y+pX2uh9jVPOemqRe3X/DHTurot1HZdqIPhILX+WjNXeK2/lsZAo2npRi8v2xRsoqi+SE/cExUMGC3URjnisaizXFnm+5RsTyYvKY9af22MNQ3tW9QWYWFzrf68ba3TOzzvb3ufT3d82qW6aItPdnzCl8WxMR9Gp+3QvEMBWFu5llA4xG7Pbvwhv+lNMITRSCCTak/dq2CyDFeGGUxm/BajMvRhqZZpX2t9ulBHp1Y2fsuWFvXfv/87l314WVxlif4N6gJ1BMKtLWrjeTCmfy4oWUAoHGpl9UaLa0dBZw2BBsqbyhmRMQKXzaVnHIwEKRoBhftLqONKeHKgIqVkbdVaRjtGd/schjUUCodMizo6taeBMc2nylsV89C2hdED9oa8rKtax/D04STZk0iyJzEyfSRD04aa+5rTs6zN07MMoU51pJodEOOaw9KGMSKj/bHDc0edy2nDTzMt15LGEvPFmt5/OoUlheZ1jGQkRpTsoNRBuG1u3QJK0TsK7/3sAl7dVM87WzczPDsNp83KFUcNZW5VLisrS5g6KJ9zj5rEj95OwZnSSIpT0FTjJNWeQZO16f/bO/P4KKt7/7/P7Ptksu8hhEAg7AKyuKAIbq1W615vsXWp163Ly95ebW29/d3ba729t1arbW21tlXritZqRYHiCiKCgGEPEEIWsieTZWaSmXl+fzzzPJlJJiHIFvC8/yE8mcycOc95zud8l/M92Ky99AG/umoOaypb+fXqStbsaWa/EiDkaOfa2QWcPSGFb6/rZFLmWJ5ffwBLUR/NbXW8+OY7uMrqEAKW79zO+2tX0+DvFxWDpRFnCeys7+PF9/dDEbyxdReLis6lPVbI5su/2sAL31rABO90wspT7G2vosA5kf/3+jbsZiM5mZ109nWyvWW7vr+8L9rH5X+7HLPRzA3lNyBiOZNaJbkp1pt5+oCLA3ipaw/w7Znf5rLSywaNm/j/a0eVatn3mlBrE4s2OVmMFjIdmdR31ScXamc2W5r7E9RAtWC9Vi/FnuIRC3V8FSp/yK/HfwPhAAZh0J+FkaItXuu76okqUUpSSqjyV9Eeasff69dL3brMLr04j1YKd4JvAv6Qn48PfoyiKIdtUbeH2jEKI26LW+/zVFsqKdYU3bU7XNZ8Z28nDpODTEem7pHY17EPm9FGSUoJD65/kCVjliT97Mq2Sv649Y/8x/z/SNhrHo+iKFS2VdIdVrdXaqEkTYimZUzj1cpX2dS0CVDvjVYZDsBp6T/RD1RX8JFsz/JZffRGe4lEI3q/lPpK+aD2g4Q+gf6tgwlCPcQ+6jV1a6jqqEoomDQU8dX1OkId+vZM6LeoNQ+LJtRaJbyBWy3jx/xw27i0e1viLdHHYCgSwmQw6S745kCzGr6LW2QfD075EqJPX/g053vP/9x/77P59AdsYJnReLQ9oHs79h7Sotb2SDf0NLCtZRuT0vprwDx78bPcNfMu/f/agNAmRo/FQ7W/mtquWv0zoT8utKhw0bCfLYTAbrLjMDtItaVS01mjD9A5OXP018XX+tbi/LmuXPWc7r5O9vv34zA5mJiRT6lvDEDCROQwq6t9u9nO3LFp3Lt4PgCBSCfXnDaBO86eDsDZE82YhJlLpuXzX5dN4b8vn0J9RxAiboQhzPcvHENRtiq+N8yZyeq7F+Kz2zEZo3znwlSEiGI2WLDb/ZRle/jjDbP5/ddn8YMLyrhrsRrz/9MHjViNqpdi+Y49VNR28P6eapSIFSVq4Gu/X8c9L6pCd9/ra/jaHz7i7W0HeWvbQX79gVpdqzfay8Z6NT63tm4tVf4qOns7+eEHP9Qr0L30ST33LNvCvcs+Y3raAqLBAh54cwc766I0NmUPip1pp6MJBFlONcQRL9Sdwb4Et61GtjObgz0HE1zf7aF2/X26+7ppDjTz5Ve+zJ72PeqZ6LZ0xvnGUdleiaIohKNhbnr7Jj6u/zjpOKnvrtddq/GTm7Y3/lAT7UC08alNgFoiWluwjZrOGr16XXxYSIvPFnmKyHfnEwgHaAm2qEJtGhyj3tm6M2lRktZgKynWFAzCoIuIz+bTn8O2UFuCOA/cR+0P+XFb3JT6SvVJf2+HenbAosJFNPQ0DGnRv1PzDq/teW3YWvsHuw/S2aeeLR+fX1DTWYPJYNLnB22chZUwtV21eh9o30n77lnOrM9lUWuLHc2jF4qE9PaUpqjGjtlgJtOeqd+jjlAHPqsvQaj1ymRxru9AOMDutt2ElfCIjnPt7u3Wv1d7qF0/9AbQw1gDXd8VLeo81RRoSniveAt7OItamwfHpYxLqAUB6hjQwjUnIk59Sgu1EIICTwEZ5uTZoCPBIAxk2lX393BWhCaatV21h3SzL8hbgEDw9LanaQo0JQi1w+xI+BxNqDURvKTkEhp6GlhbtzahRGBJSglT06cmjYkPRZ4rj5quGtbVryPPlUext1i3Ds3G/spkVf4qymxl5Dpz8Vg8+EN+artqyXfnI4RgjHcMkLiQ0SZQbdFSntXvJchy+ch2qdbAjvbN2GOibjQIrp1TyHv/dg73nq/Gx1uCLboLMN+dT77PQXGal8l5TsrHqBPLnOzZhEU7T95wGueUZbJ4Uhb/urCE2WPVz144rpCnbpiHw+TEYOriS498wKbaeuxGF8tum48QgrIMVSj2tNazv6WHJ2+Yzaf3LeaaMxQMqA/ofW/+g+UVB3nsk+ewCjdl3A3Ay1vVetcPr9rP21sbmFuSxh++PotvnTWW1zbXcc3jH3Hznz/h5j9/whOfhZj7s1W8t6tJv7c2o4On3lOtIW1Cr2ypZ85/rWLDAfW7axY1qO7tg90HdQHVxp7L7FLrPvf1sLV5K1X+KtbWraU50EyaPY1xKePoCHXQEmyhqaeJdfXrWHdw3aBxEYlGaOxp1N2d8ZO+JtSHizZ561ZLipokuaN1B8FIUB9D2vhSFEWPufpsPt1VXtNZQ1gJ9yeTxRYToUiI21bdxnVvXDfoPOb4kqvxFrUmSO2h9gTxSOb69lg9jEsZR7W/mp6+Hva172OMd4zuaRgoDhpafkD8gTCbGjfx0IaH9IWb5k6H/pLFoFqMea48PfymxYdBvQ/xMWpQF/4mYSLNlvb5LOo+9d7q4YRIUH+fsd6xmISJ8b7x+Gw+3SXeFmrDa/Ue0vW9rWWbXm9hJGVau8PdejioI9RBKBLS5xftvukWdcwbpeUYNPUk3ovK9kr9WRvWou7Yg81oI8+dNyhJ0d/r1/NDToT7+5QW6qOF9qCMRKjh0Bmx2c5s5uTM4aXd6p7neKEeyMDKZOcUnEOeK4+IEiHb0S/UqbZUnrn4mUEnhA1HviufyrZK1tavZXHRYswGs25lxFvUVqOVq1KvQgihWtS9naoV5FLFbV7uPO6Zc48eS4P+CVR76OPb6rF49KSrhp4Gbpt2W0K7zEYD2bEtIi2BFv2B1D7PYrTQG+3VV7bzcucRVsL6ZPlp46fc+NaNuivwhxfOZHKel3R7GmdNtPPfl0+hLM9EfkoqpVlu1t27iOdvOg+jMHL1PA+v3r6A+SXpCCHY27mDGVnTcZpSqO7Zwa3Pvk9F21q6W6ewZoeAqIWoWZ2E//m989hw32L+/M05+JwWbl1Ywk1nFPPQ1dO570uTeG9XM+sOhjEaBLf85RN21qnxr+6gmXV7VStOKwHZh58Mj5m/rFeteO2+gGoxNXQ30BpsxW6yk2lXrXGH2YVJqFnnz36qWsqV7ZV6JqsWUjnQeUCPW9d1Nurv2xeJsryinn1tB4koEb0SWnVHv5X6eYV6ZqZaMvXVyleBfqHe3LQZUEM2oI7jsBLG3+unLdiGQOiV5bS29yq9g7K+u/q6aOppor67nhvfulHP4QBVTDQrTLPSNNc3qOI4XIy6s7cTt9nNjMwZKCi8V/sedd11jPWO1RfxA8VBQxuT8e15tfJVnqh4Qhfo+Epz8Zn8dV115DpzSbGmIBD0hHsS5iDtu2si2dTThN1sx2PxDBLqnr4e7n737kH17uPR7q32vqFwv0XtsXiYlDaJ+bnz9TkgHA3jD/nx2XzJXd9xRYniFxkjKU7S3ddNnlO1lDtCHQkWtdlgxm126wmk2tgA1Ts10PW9t32vPv4OZVEXe4sxCMMgoe4IdTAxbSIWg0Va1KMVbdU83P7JDEeGHluymw89kV1Scole2GNi6sQhX6e7vmOfbTQYubZMTcg43KL7A8l359MSbCEcDbO4SN3rrVUFMxvN2Iw2Ct2FfPe07+peCbdFTVSp7arVTwgzG8xcN/G6hElEc8tp3gXNtQuqy3daxjTunnU3yy5ZxvWTrh/UNr2MaMyiji9FqpVR3duxlxxnju5G1SbDhzc+zMcHP9YPhdBW+Km2VHqjfq6dU4jXGSYltjI3GgQGYSDdnk5EdDAxR+3z3kgvO1p3MDV9KrOzp1OS38btl7QjDGGeu+5ONvxoCVMzy9Bq+BT6EuNWDouJH31pEl+ZkceNZxSz9p5z+eVCB6/dsYACn4M3Nrfrffrqrefqf2fGjRAKT35zEimuXpSIjW/9ZRNfefRDyn+8nN/9s4XeaC/v7ttKuM/Bxn2q4Ne3wmOr1In43Vghlbd2b6LW38jyzd08tlIV5caeRv6wRhXIN7fv4rXNdbxRsYevPLaKW5/eyFVPvAn0Z5jf/fJH/H2zaqXGC3VFbQcVtSMr3ZjjymFqxlTddZzrysVqtPJZs1pARVtgxlexaw+147V6MRqM+iJtKIu6vrseBYWvjPsKbaE2bnr7Jl0824Jtg0ry+qz9ru/2UPsgoa7tquX7735frc4Ws6hnZs3EbrLz1+1/1duc7lA9Q0NZ1Fob4i1qzf2/fN9yYIBQx1l8Dd0N6rnfBpPe1mSLYU0YmwJNOM1OPBYP3X3dCdXatrZs5a2qt3hux3NJ2wlxQh1730AkoAu+y+Li6Yue5s4Zd+pb6NpD7SgopNpSE4RaOwo43qLW7jMk1gdPhna4yFAWNahx6rCifr9UW6o+HqZkTKGpp0n3Vvh7/TQGGpmWOQ2DMNAeaqe2q5b719w/KCu9sr1S9yLZjeoY18rT+nv9pFhTGOMdIy3q0Yq2RWs4i9pkMOmW90j2mJ5XeB52k50x3jHDuspdFrVyVvx5v5eXXs6c7DmDsogPFy22k+3MZkr6FKBfIC0GC0aDkdcve10/txvUiW6/fz/BSFCfPJMx0KK2GC168ovbotZ/Xlq+VHd5DkR7bWNPo+4C1OKiVoOV3mgv+zr2MdY7Vvdm1HfXU9FcwScNnwDo+6Lj3Z3aYQzJEkIy7BkJMc6drTvpi/YxJWMKk9MnU925j2d3PcasrFlMzVC3+5X61NidSZiGTBbSSHNZcZoFaS4rf7/zDH58oVrvuywzkzxv/z7U+fnq6j8i/CwYb8NjSeGTqlYMAq6cVcB549XKSPu7KomGHUzIUF1/2e4Uxmeo/TYmR3VXdynVREWIktRsPt2nugb//dUPeWOras0JUyd3/fVT7n7vu1Rbf8X9X56E161aUbUNKeprDAH+3+vb2FbnZ+OBBurbItz+zEa+9MgHfOmRD7jj2Y20dvdPen/bVMvcn61i9Y5+ax3gvEI14cqACZfJhdfqJRAO4DK7dA+L9m9rsJXWYKsu3BajhUx7JtWd1USJDrKotXjlwvyF/Pa839LY08j33/u+/l4jdX2n2lLp6ethbd1allctZ0frDjp7O/FY1GNnZ2XNYmOjWs9/JBa15rmI3z6oJf69vf9tFEVhd9vuhCx4UN3GTYEmfYGrPZcTUyfqZ7IPtKjD0TAOk0PPfYhPkNMWsW/ue3PIg3k0odYW2aFwKEGohRAIIXSLXXPrp9nSEoTabDDrSa8am5s2MzltMnDoutyhSIiIEiHTkYlJmGgPtSdsz4L+UJB2eJB2iNGZeWeqx/LGPkMLtZSmlOKxeOgIdbC6ejUv73454XAbf6+fxp5GPRFX64NAOKDvVvBYPBR5inQP3/FECvUI0AR4uGQy6Hd/j2QrmMPs4O5Zd3PTlJuGfZ3JYOJ/zv4fLh93uX7NbXHzxPlPMDVj6iE/Zzg0l9HiosW6CGoTpWbBD0wacpvderJNvMtpIANj1NDv/h5JxmSaLY1MeyafHPwkIdkI1PvQEmhhd9tuJqZN1D0L9d31PLX1KVxmFxNTJ+oZm9qiIdWeSmvc9ixtQtPIcGTQGOgXF80KmJw2mSkZ6kJmUtokHj73Yb1fNKE+1NgYiM1sZFJsj7zb4k4oxDE5XZ3QmgPNdPS2U5yayZb7z2fZbQu4/5Jybj9T3UZnMHVyemEBSyaoVsCk7CzuOldtZ0MwViZTqJPl0tOnsPzOCxAYKcgIc065uoDyuYM8e/PppHhbwbqfgvy9XDtfbctjb6v3+bzJHpq6Qlz8yPt0BHvoCRlZub2BO88dx7cXlfL2tgZu+OPHtHX38n8rdvHt5zbR2t3LnX/9lI/2trB6ZyO/e3cPf/1nCgDhPifffWEzXT1qn4UCaVz9+EcsffJjWvzquFt/oJqGrpaE2HyuK1e3ZuJrfUN/zfQ0exrTM6dz5fgrqWiu0AvGaN6YHGcOE1MnMiNzhi7UHaF+13emI1NPxgPV/Ry/qNOq3xmEgSJPEV6rF7PBnDBu4tEEXBPKQDhAY08j+a589vv3U9FcQVVHFbOzZ+ttATXko6DoRoL2XBa6C8l2qeNdE654kXSYHPpiJD63QPv8xkCjvtAYiCbUmjUZjATp6u3CZrQlGCnaCWTaIiTN3i/UZoMZgzBgMph0i7Whu4HGnka97nt8EmQytHvhNDvxWr2DksmgP/Nb8yZ4bV4K3AW6Z0a7H7taVW/FeN94UqwpukUNcKCrP8FPc2drFrU2ZwQjQb0fvVYv6fb0QXXXjwen9Paso8VIYtTQ74oeadWmqyZcNaLXnT/m82etD0d5Wjln5Z/FVeP726FZskN91/gtRcNZ1AP3uYLq/qxoqRiRUAshWFiwkL/v/TsAc3Pm6r+zGC16NvRFxRfhsrhwW9ys2r+KipYKlpYvxWq0sr11Ox6LRxfVVFsqbaE2vfDBwO1SmY5MPbMWVJdkijWFbGc22c5sfnH2L1iQuyBBVLVs2PhJZKRoCwWPxYPZYNbPVta8G02BJtpD7fpkrREf7/favLoIeSwefeyFo2qhjCp/FaBO9EVpLjIcaUzNMeCyKKxtU63N6YUOuj5QBeLn63+Ov9dPgauQaGYWjcJBYTosnLWDXe1bSUs1U+TN5aGbLsBoUPt1Sp6XW/7yCaf/bBW9kSiXz8jj2+eVcsVv13LN4x/pbS1IdTM2bwo9vRH+tqkO9xgb2MFrykWEYVu9n3tfbIEi+MXKjVhSa8my53Pe/72L22YiZ1wG6xvVLUJKNLYTw2BBINjerH7PzVUR7npqNWOKjYQiIX0C1ixqm9HOXWWPUZriw2RQt2y1h9pxmB2YhAmf1Ud3uF+oazpr6O7r1sesVrQm35WvL84y7BlJLeqevh59Uau5vrWtjkvLl/LAxw/wndXfIayEOS3rNJbtXqYLtSas2r3XKusVuAvIceawu223vkixGq2YhImwEsZpduoW9ycNn/Dirhf57mnfpaGnAZfZRUSJ8MbeN/SFQTwDLepgOKhWjYsb76DOAV19XXofxbu+tefAYugvSqSNwdOyTsMojIeMUWvFTpxmJynWFPy9/sGu71goQDOKFhctxmKw6HN1U08T433j2dG2A4/FQ7YzO0H0od8LA/1buLTcifj9+fHFkdLt6fh7/QmV0o4HUqhHwLGwqEcDLouLRxc9mnBNd30P8V3jRVbbv5gM3aKO6wttITPQkh2KhQUL9WM54y1qbRFRllqmW7S5zly2NG8hz5XHLVNuYXvrdn67+bcJYpxqSyWqRGkJtBAIB5K6vrUH2WK0sKttF+N943WhT7Zg0j5fm9wOB+3z48tnBgNBvYrewe6D1HTWJNSd1r6HxaAm1PmsPv2eucyuhEn1rPyzqNpWBfQvwDT3vrYVJ6JE2N66HVATFVcfWM3E1Ik8cu4jZDoyuXCZD3+vn7C9Bn/XJvp67JSljdNFGuC8SVn84sppvLShhtsWjuOMUvWzXrp1Hh9WtlCS4WRCtpsUh4XW4HQi0Qh1Z5h5YtdKVtdUcu2MWdwydR71HQGu+8MHNAOzSyzsDARpaDMx3WNle30nO4JhTGnqdpkH/rGH7Tu38LW5hShRM11KC0LAT16ppig1hY92KVjz4abnloETfrm8jvrqnXx6oJ33dzdz+Yw8/u/q6diEh/f37oOImojX22ems7ebD/apld6e26xmxb9d0UHnwR1ML0gh31WoHyNb3xHAqHjZ11ZPXySKyaCWzN3T1MXFM9VxmmZL42D3QaJKVI9PT82Yyj1z7uG92vfIDeUyP3c+LrNLTybTEqJ013fMoi7wFOjzjGb1CSFwWpx0hDrUZLLY8/XAxw8QCAe4asJVHOw+SJ4rj1JfKSv2r+CHc384aDwGwgEyHZkJiVTJFrRuixsFRd+hkGZP0xeI2rwRfxyvFr/PdGTqYjkc2h5qpynRoo4/sW1gcuC/TvtXoN+zoiWS7mpVj/wVQpBiTVFPY4tlncefXFftr8ZsMOvhQK0PAuGAvnjyWD36c9QSaNFj6McDKdQjYKQWtS7Uh1kHeTShu74PYVFn2DMG1eeOZ2CMGlSruKK5Qo8nHYrTc07Xa0THW+/aZPDlsV/Wr+U4c9jVtov/XPCfuCwupmZMxWKw6NYF9D/c2gp/kFDHkgabA81kO7OpbK/kq6VfHbaN2vGTn8uijn1+fPnMqBLVC0i8c+Aduvq6BuUiCKHulz7QeYAUa4ou1G6LO8ENOiF1ApmOTBp7GvXXZNgzqOuuS6g2pmXk3lB+A9dNvI6p6VP1BZbH4sHf69djfYFwIOn4vnxmPpfPTPSwFKU5KUpLLP6j3YMMB6TXqFaulvGd47Xzt9sXsvhlJ5MLDWzb2cW3zpjKXTPncrAjyPfe3MpnIfWUt3ljs3hxwwGe/+QA7vFmhKEXodi4ZlYJ/3FpORvrM7jln08hbLEdA54MHv5nJTazgXPLMln2aS3+YB/1XQ4MphrMkSz6zCY+quzC6GxB6QtjdEBzbxWYoaYFHt+2l3BUQZiuwzc2mw3Frdz2zEY63EYM1gOc+7/vMCHLw8rtDZiNgr9t34tjDHR25NJr/YwLHnkTg3s9GCDNmsfVZZO4uuxqvW/iRUwrOKP0eXlpQw2FrgkUeYrIceboC974589ldtER6khwfWt7gKs6qvQjcRcVLuL1va+zqXHToHs40PWt7aOOf4agf7zu79ivZ2Br9Rm0ZzPeotZcxen2dN39PBya69thduC1etnYuJGu3q6EnQ/azwOLS2kFqhp7GolEI+xq28UV46/Q+3dn2069LGp8Bnxddx05zhw9ITh+25/u+rZ4CdnU56Y50CyFerSh3fwRC/VJYlEno9RXislgGuRu1dAeUm3lORSahRkv1Gfln8VZ+WeNuC0Wo4UFeQtYsX9Fwuc5zU6MwshFYy/Sr9089WYuHnsxs7LV+K3VaOWcwnMSHuRBQj0wRh33kIejYQLhgJ75PBxlqWWHjLslw2F2cN/c+5iXO09tj8Wjj510ezpbW7ZiEAb9zOx4sp3ZqlDbUvTvNVCoc5w5lKaU0tTTpLt+0x3pbGneQl+0jzSbevCJFovPdeUO2kngsXjY3babzr5OjMJIRIkMu0A7HLTJNj6h0GMzk25Po9pfTUSJ6K/J9tq448w5fGvlEwBcP2ccd54+k0dW7Wav3UlrqJsCTyYPXK7mbUzNVmOV2RlNtLXCzy+bh03Jw2IykOa0cOXv1rJyeyPlUwrwGzcyI3Mc+/3p5FgLWN+0C4+7j+YgCHMzCvDgZaczL+csKmo7WLOnhUdXV/LVXWvxOcwsmVDK2oZ9pIQsrNzewLcXlfLNBcU88P5zvH4Qcu0TqIp+Rqq3mz3BWqImFwt+9iHjs9xYzQZ6w1FuPKMYu9HNpzV1PPZOJRu7dmNQLFzwy/UoiiDT7eLei37Hw6v2suag6g35x5Zm2pv282l1Oy3dAgRUN0cIBlWx1EIf71dto67rINMypjE/dz5mg5l3D7zLLGYRjSo8/v5e/IE+esI9Ca5vrWJbMtc3wD5/Fam2VN3j5DQ7E7ZQaULdHGjGZrThMrsOS6i1GHVHqINibzHfmPwN/TXaeB4o1BajBZ/VR1OgierOaoKRoO79SLGm0NTTpO/njreo67rqEjyE8V4FrXyox+rR92onK6xzLJFCPQIcZgdzc+YOu98Z1CxQgzAkxBBPNialTWLddeuGdH1rD+lwiWSgFvYXiIRTdT4PV46/ksaexoRDUL428WssyF2gu6FAdSUOTK77n7P+JyEZTmuLFrccaFHr8a1Ak/4gjkSo75t7X8Ih84dDfJ7CLVNvSUjq2+/fz+T0yQm13zW0MZZiTSHXlctFxRcxN2duwuEIua5c5uXOw9/r1xeZGfYMWoOthCIhpmdM58O6D9nStAWTMOkLlXg8Vg/1B9X46ldLv8oLu174XPuok1GeVk6huzChZC6ok7CWNBZfkS1+IrWarMzM8/HHb8zhy684aA2RMB60+KaW2at6PvoX0L//+iw+rW6nOnKQhzb+k4aeBlKsbiZmZbCuKUh3WJ3MtX3tHqsHm9nIrDGpzBqTypLyLB5dvYdbzx7L2pZqVtd38/ytM/H3GMj2qpP85EIDrx+Eexct4ZYVL3HjQh9Pb+ulKzSWuflj2VLTgaJAsK+X772wGXtBBGFs5sHNO7Hl7cHqSOHOc0o5bUwqP3r1M77z/CYMAvKyvOCDFVu6eeOjCtKcFgx5VjDCxqoeLl3/GYXjz6SECzmg/Iw/b/gQc0oHStgLioUZGbN4t+ZdxtlncsNT6ml3AKmTutnX1MdPX1PjtQc7O9nd3MhYrzeh7Gdfnzo37Gjey4S0/nLFLrMrsdRxbB91U6CJNHua7n7W3P9DER+jnuCbQI4zh9+c95uE50BbwMWPd40MRwYNPQ3sbFNPCZzg6xdqTaSLPEXUdtUSiUYwGozUddVxdsHZ+nvoQh0J6n/jtXj16mTNQSnUo5LfL/n9IV9T4ClgxRUrkk54JxPDxeI1cTuURb2wYCHPf+n5I97rPS93nm5xaqTb0xMm5aEYmLGuCfUrla9gMVgG7V/X3rOpR03iMgjDsHXTNYaL1R8O8ROF1pZkx6RCf7w/xZqC2WDm52f9XP+d2WAmqkTJcmSxtHwpS8uXDnrf7r5uJqRO4MO6D2noaSDPlacf2xpP/GLm5qk3s+7gOj0z9khZVLSIRUWDS976bD69EEq8UMe7GuNDDdqkqrn3NXJduepZ1CQWjAFId1lZPCmLFfvVBWBlWyWn55yO0+wkrIQJh8N62AAG1/cvy/bwyLUz1L/t6S/OU+At0F/T1NOESZj0cVbfVU91ZzVn5J3B9xeU6a+LRhVe2lDD0/syCRmq+eu/LOamFU/js4/he0tUkXnjrjOpqO1gcp4Xj81MZds80q2F+ANhClLt3LZqGR/U7uHrc8dj757Am595WFkVROSm403dT08Unl3j509vvo3Rm4E1ey33bKkm0pfFf102mb1N3TzfHGTdni4ira04xsMfPthJ0NnNp1VBzvj5aiwmAyaDoLG3EnIAQ4iGNrMu4maDDaHEKhsazATDaoy6OdCsjzufzafXou+LRDnQ2kNPb4QUh5m8FLU0bXtQ3RLmNDu5ftL1XDfxOrpCkYTFgjYuknkvMxxqct/O1p2YhElPEIsX+jnZc3hx14s09jTis/loCbboFc6gP0kxEA7Qo/RgFMYEj4G0qE9yNKvsVCXdno5AHHKyNhqMTEwbupDLiUCr8NTd1803Jn9Dj0lr+Gw+TMJEU6CJfR37KHQXHjXr8XDR2jY/d37S38cL9UCcZid2kz3pvu74BU6+O1/PAYivrBePFh5It6eT7czm9cteP6zv8XnQ8iQgsXSq1Wgl3Z5Oc6A5Qai1n+P/DtTF5LaWbXit3iH3uGu5D73RXpxmZ0L8fWr6VFZWrwSG31Ko3avGQCMFnn6hbg40k2pX92vbTXa2NG+hOdA8yINgMAiuml3A7kgBb1ZVkOq00BluZqKzP+ThsZmZX9J/78b51OcvxaEuqjXLMt/rY+n88XxvseoJ+vGHH/JK5SsAnDGmhLKUEtr7XPyt5TU82av59UX3M7ugiFBfLy88G+HaWSV8qegMvvkuROjFZull5rgCzB1ejAYD4UiUPCWHDbFjnZvazVz4q/dxWk1siwiEiHL9H9ZREe0mHOnmF2/tZHtTLe0dKZT/eDm+/C46re38+8ubWbaxjt5I//nQk3I8zClO5aXKTYg0+OMH9fi7m1i3r4W9Td3ML0njiaWzsVuM+rg3KDZdwA92BPn16t20hO3U9m7FZFhPnquIh1bsZdHErIRn5WCDamjUdNXoiZXxC24hBDaTjVA4RDASxG1R4/Bmo1q9sblHCrVkFJPlzGLZJctGZGmONowGIz6bj3A0zI2Tbxz0e4MwkO5Ip7GnkV1tuyhLLUvyLseHWVmz2Nq8Vd9TPZBzCs5hX8c+fcKOx2l2DpljEO/tSbOlkW5Pp7qzemihjgnU0bKiR0K8FR3/M6iT6UCh1izqgV4WzUKKF/uBxIdwnGZnQsxzSsaUEQl1fNGTcDSsLwqaA81k2DMQQpDjzOGtqrcA9Kz+gXitXvwhv1rspKdpyHuYDK3dAxeW8SWFf3zhglgIaQLmtVfw8q6Xuf29q3n5kpf1uPDsgnHMHpOB6T0TX52VzvO7Q5xWkMOtXzpNf5/WYCtnP6/+fHphEbTZaOvp5cbxPyAUibCiohtPpg1F9PHr1ZW4Slsp9E5gXmEhy2vsRAmzbNNerpg1jgbTc/Qqfs5N+x5/+Wg/f1pbRfkkI/uj8LvVB3BbrcwuTuXs8Rn8aU0VX39yHWeWZvD+nv1ghafXNvDa6lUsKc9ixbYG2nt6wWvBltXO5qZ2Ih2zqajbw2Pv7GHuJDWPRCg23txgwjUOKtuqWbNH3QqX68qlsTPIZzUdtHT1YhQWunp76Al3JVjjTpOPN7bt4uy05EVujgVSqCWHTTJxOFlYWr6UQndh0rgvqJPu6gOr6ezt5JKSS45z6/o5r+g8zis6b8jfZzgy+MGcHyT93TkF5wzpjo/3IqTZ44R6iAzWEyHU8XkNAz0Gec48tjRtSRRq4xBCHeuD4fIk3Ba3nuDkMrsShTq2n90kTMN6VrQ+fXTTo9z34X08deFTlKeV0xRo0hcLd864k70dezl/zPmDLOr476qgUNVRpdbyP4ywkdbuga7geKGO9/b9ZN5PGOsfy4MHH+S9mvf0bOcZmao7326y0xVRhW3Q9qy4LPBF40v4enlisuOPLoTbVr5IS7CFx+44netX9XDplDJunTaJKbtP48drXuW5f53CtOxiznr+HUKREE9enM/SeWPo7g3zu8+28/xOG6t+uIRUhwWTUW3blDwv977yGeur2sj2Wpk18RomF57J3joXL2+oIctj45m7ziTFfi5Prb+Iv1fspjRlPD++7jRe31LHQ++/h30MhEMpXFpezsqg4D/+8T6RPg+2HNhWbeSG19+lM6SWJnWOE7ywYS9ed5AMrwNFUXjgzR3sbzRiNrUT7ItwvHZSS6GWfKH45uRvDvv7S0ouYXnVcjIcGVxcfPFxatXRZSgBh0T3sGZRAwnxuXg01/eJsKhtRtsg4dHENz7zfKgYtZZHMdAqH0i+K5/2UDtOizPh80pSSvTa18Md66mVF63yV2E1WvnNpt/w60W/pjnQrIv9cIsuDW3xqCVBHY5Frbm+B26d02uoW32DsvULrAXku/L5qP4j9XAXR6Y+DqwmK/s71FKnA4VaOwcgGAnqhVgGomV9Z/lU0euPUacAYLEE2dayTd/6tKttF+Xp5bhtZrrD3TjMDjLdie29fGY+l83II6qAQYAQ/X0a6I1gMRn0/f3/tuhs/m1Rf87HHeeWMiYzzD0bHqLQk89D18xi3tPpeLKCFHpTWdti4L6XD1CW7eWnl04my2PlxpWPYXWZqelsYs9BGz96tYJn1lUzYUoWWKtYUp7NO+/sGPKeHE2kUEskcVxddnXC3tZTDbPRjM/qoy3UplvUwJAWdbGnGJMwDSq6cixJtaqTv1YmMp4pGVNwGBwJrmjNuh7Kok4Wx48n351PRUtFgkVtEupBGLnOXP3wh6EQQvCfZ/wnabY0Pm38lEc+fYRlu5fRFmwblAcxHLpQt8aE2nn4ru+B25XyXHmYDKYhrfPTc07nraq3cJqdzMicoS9IbEYbFS0V2E32pHkSboubYCA4KC9Aw2K00Bfp05OutHujnwEebNML7QBsatpEeboaEuju6x70PTSEEBiTrJnslsGJkAM5r6yYezbAwhI1Qa88cyxNPfX4vB4yerK47qxx3LWoFJdVlcVUh5MMh4WISaG5xc0z66pZMimL0vElvLBzw6Dz5Y8lsta3RPIFI92RriecHcqinpA6gTXXrTmu4Q7NSksWWz634Fz+O/+/EyxfzS09UKjzXHkIxCBLeyBanNplduE0OfU2GISB8b7xw5bK1bhgzAXMzp7NtWXX4ra4+cman5Bhz+D8opGX/9WE+rU9r+G2uCl0Fx7iL/rR9joPtKhNBhOlKaUJ2xvjmZszl66+Lhp6GnS3N/R7Kb5R/o2kOyw0K3uovtUsaq20qpYboXk32kPtrKlbw8TUiWQ6MhOOwezp6xlSqI8Em8nG7dNv10NaFxVfxN6Ovbxf8z5jUwq496KJukhrrw+FQ3SF/SyZWMwPLijjoWumk2HPIBgJDjoO9VgiLWqJ5AtGpj2TUFitsHR2wdkc6Dww7L744535rgl0Mpe1EEKPp2roh64MiEU7zA5+ec4vdffzUGgnVznMDl0gNHG6f/79+l7qkeC2uPnx3B+zvXU7N025aZDbeDg0a7M12Mqt0249rMJJua5cjMKY1IJ/+NyHh9xyGV/zO/4ITafZSYY9I2FrXzzxR8cmQ7eoY/uNNUHXvmNdVx2bmzbz9Ulf50DnAX07HqgW9bGq7njrtFv1ny8qvoiHNjxEW6gtaU6HzWijormCzr5OpmWWcXVZScJ3OZ5btKRQSyRfMG6acpNev3i8bzw/XfDTE9yiRLTJ/1CxZY0lY5bgNDuTitGiwsH7tAeixXFTbakJleHg81UZvKD4Ai4ovuCw/04rreswOfha2dcO8epETs8+nZVXrkxq/Q6XlJZmT6PUV0pNZ41eGATgnjn3YDQYh/z+bosbgzAMmVFvMpjojfbqYqa5yN0WN0Zh5LHNjxFVoszLnYevxceK/StYXrWclftXsrVla9JDQ442NpONKydcyeNbHk/qUbKZbHT2dWI32ROqIGp9LIVaIpEcM7Qyq6MVs1E9HKHInTw7eiDJqtIdDtMzpvPk+U8yK2uWXoVqJAV1jjZaBvpVE65KGp8fDiHE527zLVNvoaG7IWGvuRYvHgqP2UOKNSVpkRzod323BNSjSrWKZQZh4O5Zd1PbVUu+O5852XOwGNQF1vff/T5ptjTOyDuDayZc87m+y+FyzYRreG3Pa0zLHJyDobn/Lyy+MMEzogt1sBkbR6ec7qGQQi2RSEYdL3z5Bf1wiGONEEK34EzCRLG3mEmpw5cLPhYYDUb+cfk/kpbFPJZcMObwrf9ryq7Rz+ZOhub6buppGhTHvn7S9Qn/L08vZ17OPCanT+bmqTcf11BLhiODFVesSPo7LaRy5fgrE67Hn6CVx/AVGo8WIxJqIcQFwK8AI/AHRVEeGOJ1s4GPgKsVRXkpdq0K6AQiQFhRlNG9nJdIJCeckZxZfqx47SuvnbDPPpyY9olkZtZMZmbNHPL3ZoOZ3mgvezr2DFlMR8NqtPL4ksePdhOPmDPzz8QgDIMK1HitXh4860Emp09mT8Oe49KWQwq1EMIIPAosBmqA9UKI1xRF2ZbkdT8H3kryNucoinJ8a65JJBKJ5ISgWdH7/fs5p+CcE9yaz8fiosUsLlo86LpBGLiw+EIA9jBKhBqYA1QqirIXQAjxHHApsG3A6+4EXgaOfRaARCKRSEYtV42/itNzTsdj8Qy511oyckayjzoPOBD3/5rYNR0hRB5wGfDbJH+vAG8LITYIIW75vA2VSCQSycmB0WBkrHeseojPMFXdJCNjJBZ1sl4euLHwIeAHiqJEktyUBYqi1AkhMoEVQogdiqK8N+hDVBG/BSArK4t33nlnBE0bGV1dXUf1/b6IyD48Osh+PHJkHx4dZD8eOcerD0ci1DVAQdz/84G6Aa+ZBTwXE+l04CIhRFhRlFcVRakDUBSlUQjxCqorfZBQK4ryOPA4wKxZs5SFCxce5lcZmnfeeYej+X5fRGQfHh1kPx45sg+PDrIfj5zj1YcjcX2vB0qFEMVCCAtwDZCQFqkoSrGiKGMURRkDvATcpijKq0IIpxDCDSCEcAJLgIqj+g0kEolEIjmFOaRFrShKWAhxB2o2txF4UlGUrUKIW2O/TxaX1sgCXolZ2ibgWUVRlh95syUSiUQi+WIwon3UiqL8A/jHgGtJBVpRlBvift4LHL9jdyQSiUQiOcWQp2dJJBKJRDKKkUItkUgkEskoRgq1RCKRSCSjGCnUEolEIpGMYqRQSyQSiUQyihGKMrDI2IlHCNEE7D+Kb5kOyENBjgzZh0cH2Y9HjuzDo4PsxyPnaPZhkaIoGcl+MSqF+mgjhPhEHq95ZMg+PDrIfjxyZB8eHWQ/HjnHqw+l61sikUgkklGMFGqJRCKRSEYxXxShfvxEN+AUQPbh0UH245Ej+/DoIPvxyDkuffiFiFFLJBKJRHKy8kWxqCUSiUQiOSk5pYVaCHGBEGKnEKJSCPHvJ7o9JxNCiCohxGdCiE1CiE9i11KFECuEELtj//pOdDtHE0KIJ4UQjUKIirhrQ/aZEOKe2NjcKYQ4/8S0evQxRD/eL4SojY3HTUKIi+J+J/txAEKIAiHEaiHEdiHEViHEt2PX5Xg8DIbpx+M6Hk9Z17cQwgjsAhYDNajnal+rKMq2E9qwkwQhRBUwS1GU5rhrDwKtiqI8EFv4+BRF+cGJauNoQwhxFtAF/FlRlMmxa0n7TAgxCfgrMAfIBVYC4xVFiZyg5o8ahujH+4EuRVF+MeC1sh+TIITIAXIURdkohHADG4CvADcgx+OIGaYfr+I4jsdT2aKeA1QqirJXUZRe4Dng0hPcppOdS4E/xX7+E+qAlcRQFOU9oHXA5aH67FLgOUVRQoqi7AMqUcfsF54h+nEoZD8mQVGUekVRNsZ+7gS2A3nI8XhYDNOPQ3FM+vFUFuo84EDc/2sYvoMliSjA20KIDUKIW2LXshRFqQd1AAOZJ6x1Jw9D9Zkcn4fPHUKILTHXuOaylf14CIQQY4AZwDrkePzcDOhHOI7j8VQWapHk2qnp5z82LFAUZSZwIXB7zB0pOXrI8Xl4/AYoAaYD9cD/xq7LfhwGIYQLeBn4jqIo/uFemuSa7McYSfrxuI7HU1moa4CCuP/nA3UnqC0nHYqi1MX+bQReQXXfNMRiNlrspvHEtfCkYag+k+PzMFAUpUFRlIiiKFHg9/S7E2U/DoEQwowqLs8oirIsdlmOx8MkWT8e7/F4Kgv1eqBUCFEshLAA1wCvneA2nRQIIZyxxAmEEE5gCVCB2n9LYy9bCvztxLTwpGKoPnsNuEYIYRVCFAOlwMcnoH0nBZq4xLgMdTyC7MekCCEE8ASwXVGU/4v7lRyPh8FQ/Xi8x6PpSN9gtKIoSlgIcQfwFmAEnlQUZesJbtbJQhbwijpGMQHPKoqyXAixHnhBCHEjUA1ceQLbOOoQQvwVWAikCyFqgJ8AD5CkzxRF2SqEeAHYBoSB27/oGbYaQ/TjQiHEdFQ3YhXwLZD9OAwLgH8BPhNCbIpduxc5Hg+Xofrx2uM5Hk/Z7VkSiUQikZwKnMqub4lEIpFITnqkUEskEolEMoqRQi2RSCQSyShGCrVEIpFIJKMYKdQSiUQikYxipFBLJJIRI4RYKIR4/US3QyL5IiGFWiKRSCSSUYwUaonkFEQIcb0Q4uPYWbm/E0IYhRBdQoj/FUJsFEKsEkJkxF47XQjxUeyAgVe0AwaEEOOEECuFEJtjf1MSe3uXEOIlIcQOIcQzsepNEonkGCGFWiI5xRBCTASuRj1YZToQAb4GOIGNscNW3kWt+AXwZ+AHiqJMBT6Lu/4M8KiiKNOA+aiHD4B6gtB3gEnAWNTqTRKJ5BhxypYQlUi+wCwCTgPWx4xdO+rhC1Hg+dhrngaWCSG8QIqiKO/Grv8JeDFW6z1PUZRXABRFCQLE3u9jRVFqYv/fBIwBPjjm30oi+YIihVoiOfUQwJ8URbkn4aIQ9w143XD1g4dzZ4fifo4g5xGJ5JgiXd8SyanHKuAKIUQmgBAiVQhRhPq8XxF7zXXAB4qidABtQogzY9f/BXg3duZujRDiK7H3sAohHMfzS0gkEhW5EpZITjEURdkmhPgR8LYQwgD0AbcD3UC5EGID0IEaxwb1uMPfxoR4L/CN2PV/AX4nhPhp7D3kaWkSyQlAnp4lkXxBEEJ0KYriOtHtkEgkh4d0fUskEolEMoqRFrVEIpFIJKMYaVFLJBKJRDKKkUItkUgkEskoRgq1RCKRSCSjGCnUEolEIpGMYqRQSyQSiUQyipFCLZFIJBLJKOb/A/ozgmdJdiuDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看history数据变化趋势\n",
    "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
    "plt.grid(True)\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 39us/sample - loss: 0.4849 - accuracy: 0.8280\n",
      "loss 0.48488433005809783\n",
      "accuracy 0.828\n"
     ]
    }
   ],
   "source": [
    "# 结果评测\n",
    "loss,accuracy = model.evaluate(x_test,y_test)\n",
    "print('loss',loss)\n",
    "print('accuracy',accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务三\n",
    "根据以下表格，设置网络的初始化方法和激活函数，将Test Accuracy的最终结果填入下表。\n",
    "\n",
    "| 权重初始化 | 激活函数 | Test Accuracy |\n",
    "| :--------: | :------: | :-----------: |\n",
    "|   glorot_normal   |   tanh   | 82.8% 波动明显 |\n",
    "|     he_normal     |   relu   |89.21% |\n",
    "| glorot_normal | elu | 88.51% 过拟合|\n",
    "| glorot_normal | selu | 88.80 存在过拟合|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
